Which Extreme Values Are Really Extreme?


 Silas Carroll
 2 years ago
 Views:
Transcription
1 Which Extreme Values Are Really Extreme? JESÚS GONZALO Uiversidad Carlos III de Madrid JOSÉ OLMO Uiversidad Carlos III de Madrid abstract We defie the extreme values of ay radom sample of size from a distributio fuctio F as the observatios exceedig a threshold ad followig a type of geeralized Pareto distributio (GPD) ivolvig the tail idex of F. The threshold is the order statistic that miimizes a KolmogorovSmirov statistic betwee the empirical distributio of the correspodig largest observatios ad the correspodig GPD. To formalize the defiitio we use a semiparametric bootstrap to test the correspodig GPD approximatio. Fially, we use our methodology to estimate the tail idex ad value at risk (VaR) of some fiacial idexes of major stock markets. keywords: bootstrap, extreme values, goodessoffit test, Hill estimator, Pickads theorem, VaR Risk maagemet is oe of the most importat iovatios of the 20th cetury i ecoomics. Durig the last decade fiacial markets have realized the importace of moitorig risk. The questio oe would like to aswer is: If thigs go wrog, how wrog ca they go? The variace used as a risk measure is uable to aswer this questio. Alterative measures regardig possible values out of the rage of available iformatio eed to be defied ad calculated. Extreme value theory (EVT) provides the tools to model the asymptotic distributio of the maximum of a sequece of radom variables {X }, ad i this sese this theory ca be very helpful i order to obtai a first impressio about how wrog thigs We thak participats at the Coferece o Extremal Evets i Fiace, New Frotiers i Fiacial Volatility Modellig, Witer Meetigs of the North America Ecoometric Society, ad the Departmet of Statistics, Uiversity of North Carolia Chapel Hill, especially to Ross Leadbetter ad Richard Smith for excellet commets. We are also deeply grateful to Christopher Geczy, Alfoso Novales, Michael Wolf, ad two aoymous referees for valuable suggestios ad commets. Fiacial support provided by a DGCYT grat (SEC ) is gratefully ackowledged. Address correspodece to Jesús Gozalo, Departmet of Ecoomics, Uiversidad Carlos III de Madrid, 28903, Getafe, Madrid, Spai, or Joural of Fiacial Ecoometrics, Vol. 2, No. 3, ª Oxford Uiversity Press 2004; all rights reserved. 2, DOI: /jjfiec/bh014
2 350 Joural of Fiacial Ecoometrics ca go. A deeper isight ito EVT allows us to kow ot oly the order of covergece of the maximum, but also the limitig distributio of the largest observatios of the sequece. These observatios are the mai igrediets of more iformative risk measures that have bee recetly itroduced, like value at risk (VaR) or expected shortfall. These measures are fuctios of extreme quatiles of the data distributio. Attemptig to model the tails of these distributios is troublesome ad stadard methodologies such as historical simulatio or the gaussia distributio do ot provide reliable approximatios at very high quatiles. O the other had, the methodology derived from EVT covers this gap ad produces a parametric framework to derive the VaR or ay fuctio of this extreme quatile. It is clear that the first task is to idetify which values are really extreme values. I practice this is doe by graphical methods such as the QQ plot, sample mea excess plot, or by other ad hoc methods that impose a arbitrary threshold (5%,10%,...) [see Embrechts, Klüppelberg, ad Mikosch (1997)]. These methods do ot propose ay formal computable method, ad moreover, they oly give very rough estimates of the set of extreme values. I this article we propose a formal way of idetifyig ad estimatig the extreme values of ay radom sample of size comig from a distributio fuctio, say F. These values are goig to be defied as the exceedaces of a threshold sequece {u } followig a type of geeralized Pareto distributio (GPD). The selectio of this threshold plays a cetral role i this defiitio ad i estimatig the parameters of the GPD. The sequece of extreme values depeds o the legth of the data sequece by the choice of {u }. Therefore we eed to itroduce a appropriate test to assess statistically whether the distributio fuctio of the set of extremes give by the threshold really satisfies the weak covergece to the GPD or ot, with parameters drive by F. I order to achieve this task, we propose a semiparametric bootstrap test ad study its asymptotic as well as its fiite sample performace. The fial purpose of our methodology is to achieve a reliable approximatio of F, payig special attetio to its tails. Our tail estimate provides accurate approximatios of the extreme quatiles of F, ad from them it is straightforward to calculate the risk measures itroduced i the fiacial literature. The article is structured as follows. I Sectio 1 we preset some geeral results of extreme value theory, focusig o the weak covergece of the largest observatios of a radom sequece. Sectio 2 itroduces differet approaches to select the threshold sequece ad gives a brief review of estimatio methods for the parameters of the GPD. Some simulatios show the performace of our approach i terms of tail idex estimatio. The complete defiitio of the sequece of extreme values is give i Sectio 3 by meas of a bootstrap hypothesis test. Mote Carlo simulatios provide the fiite sample performace of our proposed test. Sectio 4 presets a empirical applicatio where the risk of fiacial idexes of major stock markets is aalyzed via the tail idex ad VaR. Fially, Sectio 5 presets some cocludig remarks. Proofs are preseted i the appedix.
3 Gozalo & Olmo Which Extreme Values are Really Extreme? REVIEW OF EXTREME VALUE THEORY RESULTS The purpose of this sectio is to briefly itroduce the set of results of the socalled extreme value theory ecessary to develop the theory of the article. The departig poit is the study of the weak covergece for the sample maximum of a sequece of radom variables {X } with distributio fuctio F. Our itetio is to use the limitig distributio of this statistic to derive the weak covergece of the largest observatios of a radom sequece imposig a miimum set of assumptios o the distributio fuctio F. Let M ¼ max{x 1,..., X } be the sample maximum of the sequece ad let F be the commo distributio fuctio for {X }. Our first goal is to itroduce the coditios uder which M coverges weakly to a odegeerate distributio fuctio. Result 1 Let {X } be a idepedet ad idetically distributed (i.i.d.) sequece. Let 0 t 1ad suppose that {u } is a sequece of real umbers such that ð1 Fðu ÞÞ! t as!1: ð1þ The PfM u g!e t as!1: ð2þ Coversely, if Equatio (2) holds for some t, 0 t 1, the so does Equatio (1). The proof of this result is immediately derived from PfM u g¼f ðu Þ¼ 1 ð1 Fðu ÞÞ : ð3þ However, this result does ot guaratee the existece of a odegeerate distributio for M. Defie the right edpoit of a distributio fuctio as x F ¼ sup{x j F(x) < 1} þ1. It is clear that M! x F with probability 1 as! 1. Suppose ow that F has a jump at x F with x F < 1 (i.e., F(x F ) < 1 with Fðx F Þ¼lim x"xf FðxÞ), ad cosider a sequece {u } satisfyig Equatio (2) with 0 t 1. The either u < x F for ifiitely may values of ad (1 F(u ))! 1, oru > x F ad (1 F(u )) ¼ 0. Therefore we also eed some regularity coditio o the tail of F to avoid the existece of such jumps. Result 2 Let F be a distributio fuctio with right edpoit x F such that lim x"xf 1 FðxÞ 1 Fðx ¼ 1, ð4þ Þ ad let {u } be a sequece with u < x F ad (1 F(u ))! t. The 0 < t < 1. We will assume hereafter these regularity coditios as our miimum set of assumptios o the distributio fuctio F. The choice of the sequece {u } determies the value of t. Suppose v > u ad Equatio (2) holds, the (1 F(v ))! t 0 with t 0 < t. We ca write Equatio (2) as P{M u (x)}! e t(x), with u depedig o x. Moreover, there exist some
4 352 Joural of Fiacial Ecoometrics scalig sequeces a, b varyig accordig to F such that Pfa 1 ðm b Þxg!GðxÞ as!1, ð5þ with u (x) ¼ a x þ b ad G(x) ¼ e t(x) a distributio fuctio. This fuctio has bee fully characterized by Gedeko (1943) or de Haa (1976) via the aalysis of domais of attractio for the maximum, ad it ca be summarized as follows: Result 3 The distributio fuctio G(x) derived i Equatio (5) ca oly take three differet forms, Type I: (Gumbel) G(x) ¼ e e x, 1 < x < 1, 0 x 0, Type II: (Frechet) GðxÞ ¼ e x 1 j x > 0, j > 0 1 x 0, Type III: (Weibull) GðxÞ ¼ x < 0, j < 0 : e ð xþ 1 j The parameter j is the tail idex of F ad characterizes the tail behavior of the distributio fuctio. The three types ca be gathered i the socalled geeralised extreme value distributio, first proposed by vo Mises (1936), GðxÞ ¼e ð1þjx m s j, Þ 1 ð6þ where m is a locatio parameter, s a scale parameter, ad j 6¼ 0. This expressio boils dow to GðxÞ ¼e e ðx m s Þ whe j ¼ 0. Clearly tðxþ ¼ ð1 þ j x m s Þ 1 j i Equatio (5), ad hece ð1 Fðu ðxþþþ! ð1 þ j x m s Þ 1 j for all x, where a, b are suitable costats. This is the result we exploit i order to derive the weak covergece of the largest observatios determied by a threshold sequece u o ¼ a m þ b, with m satisfyig log G(m) ¼ 1. By doig that 1 Fðu ðxþþ 1 Fðu o Þ! 1 þ j x m s 1 This expressio ca be rewritte as Fðu ðxþþ Fðu o Þ 1 Fðu o Þ j, as!1: ð7þ! 1 1 þ j x m 1 j, ð8þ s for all x > m cotiuity poits. The threshold sequece satisfies u (x) ¼ u o þ a (x m), ad we ca defie F uo ða ðx mþþ ¼ Fðu o þ a ðx mþþ Fðu o Þ, ð9þ 1 Fðu o Þ as the coditioal excess distributio fuctio give u o with x > m. This takes us directly to the followig result: Result 4 Let y ¼ a (x m), the lim sup u o!x F ½0y<1Š jf uo ðyþ GPD j;sðuo ÞðyÞj ¼ 0, ð10þ
5 Gozalo & Olmo Which Extreme Values are Really Extreme? 353 with 8 GPD j;sðuoþðyþ ¼ 1 1 þ j y 1 j >< if j 6¼ 0 sðu o Þ, ð11þ >: 1 e y sðuoþ if j ¼ 0 the geeralized Pareto distributio ad s(u o ) ¼ sa. This result is kow as Pickads (1975) theorem. Pickads proposed a sequece u o take i the iterval [b, b þ1 ], with b the suitable sequece i Equatio (5). This approximatio for the distributio of the largest observatios regarded as the exceedaces of a threshold sequece ca be improved whe the tail of F decays at a polyomial rate. Suppose 1 FðxÞ ¼x 1 j LðxÞ with L(tx)/L(x)! 1asx! xf ad j > 0, the the distributio fuctio F satisfies 1 FðtxÞ lim x"xf 1 FðxÞ ¼ t 1 j, t> 0: ð12þ This type of distributio fuctio is regularly varyig at a rate 1 j ad the domai of attractio of the sample maximum is the Fréchet distributio [see Resick (1987) or de Haa (1976)]. The fuctio L(x) is said to be slowly varyig ad is itroduced to iclude the deviatios of F from the Pareto probability law. Whe these departures from the polyomial law are small, F uo ðyþ is better approximated by the Pareto distributio fuctio. Cosider a sequece u (x) ¼ u o x, where u o ¼ u (1) is the threshold sequece that satisfies 1 Fðu o Þ¼u 1 j o Lðu o Þ. The coditioal excess distributio fuctio defied by u o as F uo ðu ðxþþ¼ FðuðxÞÞ FðuoÞ 1 Fðu o Þ satisfies F uo ðu ðxþþ!1 u 1 ðxþ j, as!1, ð13þ u o for u (x) u o or equivaletly for x 1. This covergece holds for all cotiuity poits of F ad therefore for this case we ca rewrite the previous result as lim sup j F uo ðyþ PD j ðyþj¼0, ð14þ u o!x F ½u o y < 1Š with y ¼ u (x) ad PD j ðyþ ¼1 ð y u o Þ 1 j. Fially, the choice of the threshold sequece also has a effect o the error made by the approximatios claimed i Pickads theorem. This error arises from the asymptotic relatio (1 F(u ))! t ad from the approximatio of F (u )by the expoetial distributio. The latter approximatio is of order o( 1 ) sice 0 e x 1 x 1 0:3 1, for 0 x [see, e.g., Leadbetter, Lidgre, ad Rootzé (1983)]. Nevertheless, if F is cotiuous oe ca always obtai a equality i Equatio (2) by takig u ¼ F 1 ðe t Þ ad makig the approximatio errors vaish. However, sequeces of
6 354 Joural of Fiacial Ecoometrics type u (x) ¼ a x þ b, with a, b suitable costats are more appropriate to study the weak covergece of M. I these cases, the equality or ay uiform boud for all x are ot usually feasible i Equatio (5). 2 THRESHOLD CHOICES TO DEFINE THE EXTREME VALUES The last sectio has focused o fidig the asymptotic laws that rule the largest observatios of a radom sequece from a distributio fuctio F. This set of observatios is defied by meas of a threshold sequece ad the tail idex j that characterizes the correspodig geeralized Pareto or Pareto. The choice of this sequece is troublesome sice u o! x F whe!1, but at a appropriate rate. This order of covergece depeds o F represeted by the sequeces a ad b whe u (x) is of the form u (x) ¼ a x þ b. Hece the threshold sequece u o ca be defied by the scalig sequeces a, b ad the value of x satisfyig the coditio log G(x) ¼ 1, or equivaletly (1 F(u o ))! 1. For ease of otatio we will use hereafter u istead of u o to deote the threshold sequece satisfyig these coditios. This sequece is immediately derived by direct calculatios whe F is kow. Cosider as a example the case F(x) ¼ 1 e x. By cotiuity of F we ca choose u ðxþ ¼F 1 ð1 tðxþ Þ with t(x) > 0, ad hece u (x) ¼ log t(x) þ log. Equatio (2) is writte as PfM log tðxþþlog g!e tðxþ, ad the P{M log x}! e e x, with t(x)¼e x for all x 2 R. The scalig costats are a ¼ 1, b ¼log, ad hece the threshold sequece is u ¼ log, sice log G(0) ¼ 1. More examples ca be foud i Leadbetter, Lidgre, ad Rootzé (1983). I geeral, F is ukow, ad i this settig either the theoretical derivatio or the direct compariso of differet threshold choices is possible. This compariso is udertake by aalyzig the properties of the tail idex estimator of F, as most of these estimators for j are tied to a threshold choice. Therefore their biases ad variaces are iflueced by the effect of the selectio of u. There is a large amout of literature i tail idex estimatio [chapter VI of Embrechts, Klüppelberg, ad Mikosch (1997) gives a excellet review]. Amog these estimators, the most popular are Hill s estimator (1975) ad Pickads s estimator (1975). The former is give by ^j ðhþ ðu Þ¼ 1 k X i¼ kþ1 log x ðiþ, x ð kþ ð15þ with u ¼ x ( k), x ( k+1) x () deotig the icreasig order statistics ad k a iteger value i [1, ]. Pickads s estimator for the tail idex is ^j ðpþ ðu Þ¼ 1 logð2þ log x ð kþ1þ x ð 2kþ1Þ, ð16þ x ð 2kþ1Þ x ð 4kþ1Þ
7 Gozalo & Olmo Which Extreme Values are Really Extreme? 355 ad ^s ðpþ ðu Þ¼ x ð 2kþ1Þ x ð 4kþ1Þ R log2 0 e^j ðpþ ðx ð 4kþ1Þ Þt dt, ð17þ for the variace, with u ¼ x ( 4kþ1) ad k ¼ 1,..., /4. There are some features of both estimators that are worth metioig. These estimators are heavily depedet o the threshold choice u, ad both of them ca be derived uder the assumptio that F u is exactly Pareto with parameter j or geeralized Pareto with parameters j ad s(u ). Moreover, if F u ¼ PD j, Hill s estimator is the maximumlikelihood estimator of j iheritig the correspodig asymptotic properties: cosistecy ad ormal distributio. This approach is oly valid for regularly varyig distributio fuctios, that is, j > 0, otherwise the asymptotic properties of this estimator vary accordig to F [see Davis ad Resick (1984)]. Pickads s estimator for the tail idex is obtaied assumig F u ¼ GPD j;sðu Þ ad takig the iverse of the parametric GPD. This estimator is cosistet ad also coverges to a ormal distributio; but it is very sesitive to the choice of u. Alteratively, uder the latter parametric assumptio o F u we ca obtai the maximumlikelihood estimator for the parameter j ad s(u ) of the GPD. I this case there is ot a closed expressio for the maximumlikelihood estimators of these parameters, ad we have to rely o umerical procedures [see Press (1992)]. The maximumlikelihood estimator for the tail idex is cosistet ad asymptotically ormal for j > 1 2, as is discussed i Smith (1985). The threshold selectio is carried out by studyig the measquared error of these j estimators, as u is varies. However, some explicit form is required for the distributio fuctio F. Uder the assumptio 1 FðxÞ ¼Cx 1 j ½1 þ Dx b þ oðx b ÞŠ, ð18þ where j > 0, C > 0, b > 0, ad D is a real umber, Hall (1982) proposed estimators for the tail idex based o a optimal choice of itermediate order statistics as cadidates for the threshold sequece. Nevertheless, the pioeerig work for threshold selectio is Pickads (1975), where F satisfies the regularity coditios of Result 2, but ot ecessarily Equatio (18). The estimatio of the tail idex ad the threshold selectio are doe i a sigle step. Pickads proposed as a cadidate for the threshold the order statistic of a sample {x } that miimizes the distace d 1 ivolvig the distributio fuctios F u ; ad. The empirical GPD^j ðpþ ðu Þ;^s ðpþ ðu Þ coditioal excess distributio fuctio F u ;ðxþ with x > u is defied by F u ;ðxþ ¼ X i¼1 1 fu < x i xg P j¼1 1, ð19þ fx j >u g or equivaletly, via the trasformatio y¼a (x u ) > 0, by F u ;ðyþ ¼ X i¼1 1 f0<yi yg P j¼1 1 : ð20þ fy j >0g
8 356 Joural of Fiacial Ecoometrics The distace d 1 ca be writte as a fuctio of a variable u, oce is give, as d 1 F u;, ¼ sup j F GPD^j ðpþ ðuþ;^s ðpþ ðuþ u; ðyþj: ð21þ ðyþ GPD^j ðpþ ðuþ;^s ðpþ ðuþ 0y<1 The optimal threshold is the u ðpþ ¼ arg mi u d 1 F u;, GPD^j ðpþ ðuþ;^s ðpþ ðuþ, ð22þ with u takig values alog the ordered sample x (3/4) x (). More specifically, u ðpþ ¼ x ð kþ with k!1,!1, ad k ¼ o() to beefit of a icrease i the sample size. Alteratively we propose a versio of the distace d 1 where the umber of tail observatios is weighted differetly. This ew approach accouts for the estimatio pitfalls that derive from the lack of observatios whe u gets close to x F. Defiitio 1 Let F u, be the empirical versio of F u ad the distributio GPD^j ðmlþ ðuþ;^s ðmlþ ðuþ fuctio of the largest observatios with parameters estimated by maximum likelihood (Ml). Defie the weighted Pickads distace d WP as d WP F u;, GPD^j ðmlþ ðuþ;^s ðmlþ ðuþ ¼ k «sup j F u; ðyþj, ð23þ ðyþ GPD^j ðmlþ ðuþ;^s ðmlþ ðuþ 0y<1 with 0 «1 2 ad k ¼ P j¼1 1 fx j > ug. The parameter «determies the weight assiged by the distace d WP to the tail observatios defied by the correspodig u. Notice that this distace is the oe used by Pickads whe «¼ 0, ad the KolmogorovSmirov (KS) statistic [Kolmogorov (1933)] whe «¼ 1 2. The correspodig threshold choice is the order statistic that miimizes the distace, u ðwpþ ¼ arg mi u d WP F u;, GPD^j ðmlþ ðuþ;^s ðmlþ ðuþ, ð24þ with u takig values alog the ordered sample x (1) x (). The parameter «ca be useful to study the effect of differet weightig schemes i the threshold selectio; however, this is far beyod the scope of this article, where we will oly focus o the value «¼ 1 2 (KS statistic). It is clear that threshold values far from x F produce biased estimates of the tail idex. O the other had, u close to the right edpoit will result i iefficiet estimates of j. Goldie ad Smith (1987) ad Smith (1987) derive the asymptotic distributio fuctios of both the maximumlikelihood ad Hill estimators of the tail idex for a class of distributio fuctios such that 1 FðxÞ ¼x 1 j LðxÞ, where L(x) are slowly varyig fuctios of differet types. They also discuss i detail asymptotic bias ad variace for these estimators ad fid that departures of F from a Pareto distributio fuctio lead to biased ad iefficiet estimates of the tail idex for both estimators. As a result, a right choice of the threshold sequece turs out to be of critical importace i order to miimize the measquared error (MSE). Hall (1982) derives a aalytical expressio for the MSE of Hill s estimator whe F satisfies Equatio (18). All these results are achieved for determied
9 Gozalo & Olmo Which Extreme Values are Really Extreme? 357 classes of distributio fuctios. I cotrast, uder the regularity coditios of Result 2 it is ot possible to derive aalytically the MSE expressio for the tail idex estimator. Therefore we propose bootstrap cofidece itervals i order to measure the bias ad ucertaity of the differet tail idex estimators we cosidered. The aïve oparametric bootstrap is cosistet p sice the empirical distributio fuctio F is a cosistet estimator of F ad ffiffi k ð^j ðiþ ðuðlþ Þ jþ, i ¼ H, Ml, P, ad l ¼ P, WP, Ah (ad hoc) coverges weakly to a ormal distributio, with k beig the umber of exceedaces over u. The the bootstrap approximatio J (x, F )to the true samplig distributio fuctio J (x, F) of this statistic ca be used to produce cofidece regios, at the 1 a level, i the followig way, j 2 h^j ðiþ ðuðlþ Þ 1 pffiffi J 1 1 a k 2,F ðiþ, ^j ðuðlþ Þ 1 a pffiffi J i 1 k 2,F, ð25þ where J 1ð1 a,f Þ is the 1 a bootstrap quatile. To implemet Equatio (25), the bootstrap approximatio is estimated by ^J ðx, F Þ¼ 1 B X B j¼1 1 pffiffi f kð^j ðiþ j; ðuðlþ j; Þ ^j ðiþ ðu ðlþ ÞÞ xg, where B is the umber of bootstrap iteratios, j; ðuðlþ j; Þ the correspodig estimator for the bootstrap sample j, ad u ðlþ j; the correspodig threshold choice. The fiite sample performace of the differet estimators is aalyzed i Table 1. The threshold u is chose by both methods, Pickads ad weighted Pickads with e ¼ 1 2. To emphasize the importace of the threshold selectio to estimatig the tail idex, a ad hoc threshold (u ðahþ ¼ x ð Þ) is also icluded i the aalysis. The simulatio experimet of Table 1 is doe for differet Studet tdistributios, where the tail idex j is well approximated by the iverse of the degrees of freedom [see chapter III of Embrechts, Klüppelberg, ad Mikosch (1997). Before discussig the results of this Table 1 it is importat to otice that although F is kow, we replace it with F to calculate the bootstrap approximatio J (x, F ). The reaso for doig this is that the bootstrap procedure works ^j ðiþ ð26þ Table 1 Bootstrap cofidece itervals I. ^j ðmlþ ðu ðwpþ ^j ðpþ ðuðpþ ^j ðmlþ ðu ðahþ t 1 (j 1) t 5 (j 0.2) t 10 (j 0.1) t 30 (j 0) Þ [0.70, 1.69] [ 0.17, 0.24] [ 0.28, 0.39] [ 0.43, 0.68] Þ [0.29, 1.06] [ 0.39, 0.08] [ 0.63, 0.06] [ 0.64, 0.17] Þ [0.34, 1.75] [0.19, 0.91] [ 0.26, 0.33] [ 0.28, 0.57] Bootstrap cofidece itervals at a sigificace level a ¼ 0:05 for differet estimators of the tail idex: ^j ðmlþ ðu Þ with u estimated by d WP ad by u ðahþ ðpþ ¼ x ð Þ; ad ^j ðuðpþ Þ with u estimated by d 1. B ¼ 1000 bootstrap samples of size ¼ 1000 are draw from a sigle sequece geerated from t, with ¼ 1, 5, 10 ad 30.
10 358 Joural of Fiacial Ecoometrics Table 2 Bootstrap cofidece itervals II. ^j ðmlþ ðu ðwpþ ^j ðhþ ðu ðwpþ t 1 (j 1) t 5 (j 0.2) t 10 (j 0.1) t 30 (j 0) Þ [0.70, 1.69] [ 0.17, 0.24] [ 0.28, 0.39] [ 0.43, 0.68] Þ [0.82, 1.23] [0.08, 0.37] [ 0.42, 0.23] [0.04, 0.20] Bootstrap cofidece itervals at a sigificace level a ¼ 0.05 for differet estimators of the tail idex whe u ðwpþ ðmlþ is obtaied from GPD j;sðuþ ad from PD j, respectively. Note ^j ðu ðwpþ ðhþ Þ is ^j ðu ðwpþ Þ for the PD j case. B ¼ 1000 bootstrap samples of size ¼ 1000 are draw from a sigle sequece geerated from t, with ¼ 1, 5, 10, ad 30. eve whe F is ukow ad we oly have a realizatio from the radom sequece {X }. There are two clear results from Table 1: First, the cofidece itervals for our estimator cotai the true tail idex, somethig that does ot occur for Pickads s method; ad secod, the cofidece itervals estimated from the ad hoc threshold are wider tha the oes derived from our method whe j is sigificatly greater tha zero. Table 2 aalyzes i more detail the advatages of the weighted Pickads method for selectig u whe the data come from heavytailed distributios. I this case the GPD j;sðuþ is replaced by the PD j i Defiitio 1 ad Equatio (24). From Table 2 we coclude that whe we are dealig with heavytailed distributios (j > 0), our method is more efficiet with PD tha with GPD. These simulatio results are i lie with the theoretical fidigs derived i Smith (1987). 3 HYPOTHESIS TESTING Differet threshold choices defie differet sets of possible extreme values of a particular sequece {X }. I this article the observatios exceedig a certai threshold are cosidered extreme values oly if they are distributed as a GPD j;sðuþ, with j the tail idex of F. I order to check this coditio we propose a goodessoffit test for the followig hypothesis: H ;0 : the sample fðx 1 u Þ þ,..., ðx u Þ þ g is distributed as GPD j;sðu Þ versus a geeral alterative of the form H ;1 : the sample fðx 1 u Þ þ,..., ðx u Þ þ g is ot distributed as GPD j;sðu Þ with u 2 R, j the tail idex of F ad (x) þ ¼ max(x, 0). A atural goodessoffit test statistic is the KS statistic [for other goodessoffit criteria see Aderso ad Darlig (1952)], p R k ðy;j, sðu ÞÞ ¼ ffiffi k sup j P k ðyþ GPD j;sðuþðyþj, 0y<1 with k ¼ P j¼1 1 fx j >u g ad P k the empirical distributio fuctio of the observatios exceedig u. Whe the parameters are kow, the asymptotic distributio ð27þ
11 Gozalo & Olmo Which Extreme Values are Really Extreme? 359 of this test statistic is tabulated ad the critical values ca be derived. If the parameters are ukow, but cosistetly estimated, the bootstrap distributio fuctio is a reliable approximatio of the true samplig distributio of R k (y; j, s(u )). I this case it ca be proved [see Romao (1988)] that the bootstrap critical values are cosistet estimates of the actual oes. Our iterest, however, does ot lie i the defiitio of the extreme values of a particular sequece {X }, but i the defiitio of the extreme values of ay sequece of legth with distributio fuctio F. I this case a differet hypothesis test is eeded to determie whether the selected threshold is a good cadidate to defie the extremes of F give the sample size. More formally, the testig problem uder cosideratio is H 0 : F u ¼ GPD j;sðuþ versus a geeral alterative H 1 : F u 6¼ GPD j;sðuþ, with j beig the tail idex of F. Now we ca formally defie the set of extreme values of ay sequece with distributio fuctio F. Defiitio 2 Let {X } be ay sequece of a distributio fuctio F. The extreme values of ay sequece of legth from this distributio are give by the observatios exceedig the threshold u, ad satisfyig F u ¼ GPD j;sðu Þ. The test statistic i this case is a versio of the family of KS test statistics, p T ðy ; j, sðu ÞÞ ¼ ffiffiffi sup j F u ;ðyþ GPD j;sðu ÞðyÞj, ð28þ 0y<1 with y i ¼ (x i u ) þ, i ¼ 1,...,. This statistic depeds o u, j, ad s(u ). I order to derive the asymptotic distributio of Equatio (28) ad to assess the bootstrap approximatio, the followig results are required. Let Pfl < T tg U l ðtþ ¼ ð29þ PfT > lg be the coditioal excess distributio fuctio, with parameter l o [0, 1], of a uiform [0, 1] radom variable T. Its empirical couterpart X 1 fl<ti tg U l; ðtþ ¼ 1 P 1 i¼1 j¼1 1, ð30þ ft j >lg p with t 1,..., t ad t 2 [0, 1], defies a empirical process B ðtþ ¼ ffiffiffi ðul; ðtþ pffiffiffi U l ðtþþ similar to the uiform empirical process ðu ðtþ UðtÞÞ. It is well kow that the latter coverges weakly to the distributio of a meazero gaussia process Z U () [see chapter V of Pollard (1984)]. By a aalogue reasoig, it is p immediate to derive the probability law of the process S ðyþ ¼ ffiffiffi ðfu ;ðyþ F u ðyþþ, where the threshold u plays the role of the parameter l.
12 360 Joural of Fiacial Ecoometrics Theorem 1 Cosider a cotiuous ad strictly icreasig distributio fuctio F ad a threshold u, with u < x F. The empirical process S (y) coverges weakly to the distributio of a meazero gaussia process Z Fu ðþ with covariace fuctio covðz Fu ðy 1 Þ,Z Fu ðy 2 ÞÞ ¼ ðfðmiðy 1,y 2 ÞÞ Fðu ÞÞ ðfðy 1 Þ Fðu ÞÞðFðy 2 Þ Fðu ÞÞ ð1 Fðu ÞÞ 2, ð31þ with y 1, y 2 2 R. Moreover, uder the ull hypothesis H 0, this empirical process takes the p form ffiffiffi ðfu ;ðyþ GPD j;sðuþðyþþ ad the covariace fuctio becomes covðz Fu ðy 1 Þ; Z Fu ðy 2 ÞÞ ¼ GPD j;sðuþðmiðy 1 ; y 2 ÞÞ 1 Fðu Þ GPD j;sðuþðy 1 ÞGPD j;sðuþðy 2 Þ: ð32þ By the cotiuous mappig theorem, the limitig distributio fuctio, deoted by L(x, F), of the test statistic T is the distributio of the supremum of a meazero gaussia process with the covariace fuctio of Equatio (32). The proof is i the appedix. I order to test H 0, we should be usig the followig rejectio criteria: ft ðy ; j, sðu ÞÞ > L 1 ð1 a; FÞg, ð33þ where L 1 ð1 a,fþ is the 1 a quatile of the exact fiite sample distributio L (x, F) of the statistic T. This distributio L is clearly ukow, ad i practice has to be approximated by the asymptotic distributio L(x, F). This limitig distributio takes a complicated form ad depeds o the kowledge of F, o the parameters of the GPD, as well as o the threshold u. The uisace parameters depedecy forces us to look for a alterative method to approximate the distributio L (x, F). 3.1 Bootstrap Approximatio Let L (x, Q ) be the bootstrap distributio that approximates L (x, F), ad L 1 ð1 a, Q Þ the bootstrap quatile that approximates the correspodig fiite sample distributio quatile L 1 ð1 a,fþ. I order for the bootstrap to be cosistet, Q has to satisfy certai coditios. Lemma 1 Let Q be a estimator of F based o {x 1,..., x } that satisfies sup x2r j Q ðxþ FðxÞj! p 0 wheever F 2 H 0, ad let L(x, F), the limitig distributio of the test statistic T, be cotiuous ad strictly icreasig. The PfT > L 1 ð1 a,q Þg!a, as!1: ð34þ The aïve oparametric bootstrap from Q ¼ F fails to produce cosistet estimates of a distributio fuctio uder H 0 if F does ot belog to the ull. O the other had, the parametric bootstrap from the GPD j;sðuþ [see Equatio (27)] fails to capture the structure of F for the observatios smaller tha the threshold u.
13 Gozalo & Olmo Which Extreme Values are Really Extreme? 361 To fulfill the coditios of Lemma 1 correspodig to Q ad therefore to solve the two previously metioed problems, a semiparametric bootstrap methodology is itroduced. Defie ( F ðxþ x u Q ðxþ ¼ GPD j;sðu Þðx u ÞþF ðu Þð1 GPD j;sðu Þðx u ÞÞ x > u : ð35þ This distributio fuctio is derived from the coditioal probability theorem, sice PfX xg ¼PfX u gpfx x j X u gþpfx > u gpfx x j X > u g, ð36þ where P{X u } is cosistetly approximated by F (u ), ad uder the ull PfX x j X > u g¼gpd j;sðu ÞðyÞ with y ¼ x u. Deote fx g a bootstrap sample obtaied from Q ad cosider the trasformed bootstrap sample y i ¼ x i u with i ¼ 1,...,. The value of the test statistic is t ðy 1,...,y ; j, sðu ÞÞ ad for the sake of otatio is deoted as t ðy ; j, sðu ÞÞ. The bootstrap approximatio L (x, Q ) is the estimated by the empirical distributio of the B (umber of bootstrap samples) values of T, ^L ðx, Q Þ¼ 1 B X B j¼1 1 ft ;j ðy;j;sðuþþxg : ð37þ The 1 a quatile of ^L ðx, Q Þ is the order statistic t ;ðdð1 aþbeþ ðy ; j, sðu ÞÞ of the sequece ft ;j ðy ; j, sðu ÞÞg of B elemets, where dxe is the upper iteger part of x. The rejectio criteria Equatio (33) is replaced ow by ft ðy ; j, sðu ÞÞ > t ;ðdð1 aþbeþ ðy ; j, sðu ÞÞg, ð38þ ad hece for a sample {x }, the ull hypothesis is rejected if t (y 1,..., y ; j, s(u )) is i this rejectio regio. This meas that the coditioal excess distributio fuctio defied by u is ot a GPD j;sðu Þ, ad accordig to our defiitio these cadidates for extreme observatios are ot really extreme. Recall that util ow we have assumed the parameters to be kow. Nevertheless this coditio is rarely satisfied i practice. To make our test operatioal, we replace these parameters with their maximumlikelihood estimators, ad istead of Q, we defie its couterpart distributio fuctio ^Q : ( F ðxþ x u ^Q ðxþ¼ GPD^j ðmlþ ðu Þ, ^s ðmlþ ðu ðx u Þ ÞþF ðu Þð1 GPD^j ðmlþ ðu Þ, ^s ðmlþ ðu ðx u : Þ ÞÞ x>u ð39þ Notice that the ew bootstrap distributio pffiffi fuctio L ðx, ^Q Þ boils dow to L (x, Q ) for x u, ad for x > u, the former k coverges to the latter, where k is the umber of observatios of the tail defied by u. Moreover, if F belogs to the
14 362 Joural of Fiacial Ecoometrics ull hypothesis defied by u, the coditios i Lemma 1 still hold ad the rejectio regio of Equatio (38) becomes ^j ;ðmlþ f^t ðy ; ^j ðmlþ ðu Þ, ^s ðmlþ ðu ÞÞ > t ;ðdð1 aþbeþ ðy ; ^j ðmlþ where ^T ad ðu Þ, ^s ;ðmlþ ;ðmlþ ^j ðu Þ, ^s ;ðmlþ ðu ÞÞg, ð40þ ðu Þ, ^s ðmlþ ðu Þ are calculated from the origial sample {x }, ad ðu Þ are estimated from the correspodig bootstrap sequeces. 3.2 Fiite Sample Performace: Empirical Power The power of our test, Pf^T > L 1 ð1 a, ^Q Þg, ð41þ depeds o three key parameters: the threshold choice, the distributio fuctio F, ad the legth of the sequece. To calculate this power it is importat to realize ðmlþ that the maximumlikelihood estimates ^j ðu Þ, ^s ðmlþ ðu Þ that are etered i the expressio of ^T are the oes used to defie the ull distributio ^Q. This test lies i costructig a distributio fuctio ^Q, such that its coditioal excess distributio is a GPD^j ðmlþ ðu Þ, ^s ðmlþ ðu Þ. I that way the observatios comig from the ull hypothesis are draw from ^Q ad ot from F. The empirical size of the test is calculated from the former distributio. For a deeper isight ito how to calculate the power via bootstrap [see Bera (1986) ad Romao (1988)]. The followig algorithms are devoted to describig the simulatio experimet. Algorithm 1 geerates bootstrap samples fx g from the distributio fuctio ^Q ad calculates the empirical bootstrap approximatio of L (x, F). The threshold value u ad the maximumlikelihood estimates are obtaied from a particular sample {x } from F ad are used to costruct ^Q. Algorithm 1 (Bootstrap Procedure) 1. l ¼ Geerate x 1;l,...,x ;l draw from ^Q. ðmlþ ^j ðu Þ ad ^s ðmlþ ðu Þ from the bootstrap sample. ðu Þ, ^s ðmlþ p ðu ÞÞ¼ ffiffiffi sup0y<1 jf u ;ðyþ GPD^j ðmlþ 3. Calculate 4. t ;l ðy ðmlþ ; ^j with y ¼ x u. 5. lþþ. Go to step 2 while l B. 6. ^L ðx, ^Q Þ¼ 1 BP B j¼1 1 ft ;j ðmlþ ðy;^j ðu Þ;^s ðmlþ ðu ÞÞ xg ðu Þ;^s ðmlþ ðu Þ ðyþj I practice, the pvalue replaces the rejectio criteria give i Equatio (40). The empirical pvalue is p ¼ 1 B X B j¼1 1 ft ;j >^t g, with ^t obtaied from the sample {x }. The probability of Equatio (41) caot be directly derived, ad we have to rely o Mote Carlo simulatios to calculate it. The followig algorithm describes how to implemet this procedure. ð42þ
15 Gozalo & Olmo Which Extreme Values are Really Extreme? 363 Algorithm 2 (Empirical Power) 1. j ¼ Let {x 1,j,..., x,j } be a sample from F ad obtai u, 3. Costruct ^Q ad ^L ðx, ^Q Þ as i Algorithm 1. ðmlþ ^j ðu Þ ad ^s ðmlþ ðu Þ. 4. Geerate fx 0 1,...,x0 g from a distributio fuctio F Calculate ^t ðx 0 ðmlþ ; ^j ðu Þ, ^s ðmlþ ðu ÞÞ if F 1 6¼ F. Otherwise ^t ðx 0 ðmlþ ; ^j ðu Þ, ^s ðmlþ ðmlþ ðu ÞÞ with ^j ðu Þ, ^s ðmlþ ðu Þ from fx 0 g. 6. Calculate the pvalue as i Equatio (42). 1 if p < a 7. d j ¼ 0 otherwise: 8. jþþ. Repeat while j m. 9. ^a ¼ 1 P m m j¼1 d j. As!1, the estimate ^a approaches the size of the test if the threshold u is really defiig the extremes of F for a give legth. O the other had, whe the coditioal distributio fuctio defied by the threshold is ot a GPD j;sðuþ, or the sequece of data does ot come from F, the estimate ^a teds to oe. Table 3 gives the simulatio results of the empirical power for a family of Studet tdistributio fuctios with the threshold u obtaied by our weighted Pickads method. Table 3 poits out two clear results. First, the fact that the diagoal is very close to the omial size reveals that our procedure performs very well i capturig the extremes of sequeces of legth comig from F 0 (distributio fuctio uder H 0 ). Secod, extreme value cadidates comig from F 1 (distributio fuctio uder H 1 ) are rejected as extreme values of F 0. A byproduct of this table is that our test ca be cosidered a goodessoffit test via the tails. I priciple our test is more sesitive tha stadard KS statistics i detectig deviatios i the tails [see Maso ad Schueemeyer (1983)]. Aother alterative to selectig the threshold is to choose a fixed order statistic. I this case, the set of extreme values is defied by a fixed umber of observatios give the sample size. Table 3 Empirical power for differet distributios. F 0 t 30 (j 0) t 10 (j 0.1) t 5 (j 0.2) t 1 (j 1) t t t t F 1 Empirical power of T for a family of Studet tdistributio fuctios, with u from d WP. F 0 deotes the datageeratig process ad F 1 the distributio uder the alterative hypothesis. Bootstrap replicatios B ¼ 1000, Mote Carlo simulatios m ¼ 1000, ¼ 1000, sigificace level a ¼ 0.05.
16 364 Joural of Fiacial Ecoometrics Table 4 Empirical power for ad hoc thresholds. F 0 x (700) x (800) x (900) x (950) t t t t Empirical power for a family of Studet tdistributio fuctios, with differet ad hoc threshold choices for a sample size ¼ F 0 deotes the datageeratig process. Bootstrap replicatios B ¼ 1000, Mote Carlo simulatios m ¼ 1000, sigificace level a ¼ The message from Table 4 is clear: These ad hoc selectios of the set of extreme values ca be valid for particular sequeces of F 0, but i geeral are rejected to defie the extremes of ay sequece of F 0 with the same legth. 4 EMPIRICAL APPLICATION: VaR ESTIMATION IN FINANCIAL INDEXES A importat applicatio of the semiparametric approximatio ^Q of F is quatile estimatio i the tail regio, where there is usually a lack of observatios because we are dealig with extremal evets. This questio is becomig of primary importace i a wide variety of research fields, icludig fiace, climatology, ad hydrology. The goal of this sectio is to obtai a deeper isight ito risk maagemet for fiacial idexes of differet major markets. Market risk maagemet is iheretly related to the probability of occurrece of extreme evets, that is, very large egative or positive returs. We focus o a particular measure of this market risk: value at risk (VaR), the amout of moey ecessary to provide the istitutio with coverage agaist losses that ca occur with a p probability over some holdig period. It is ot our itetio to get ito details of the VaR methodology; we oly pursue it to preset some results about tail idex estimatio (tail behavior) ad a aïve calculus of VaR uder i.i.d. assumptios for fiacial data. Of course, we kow this assumptio is urealistic ad we should go a step further regardig heteroskedastic coditioal volatility models, but this is left for future research. Geeral practitioers calculate VaRs i two differet ways: (i) complete parametric, where it is assumed a uderlyig distributio (ormal, studet s t, etc.), ad (ii) fully oparametric, where the mai actor is the empirical distributio F. Our approach ca be cosidered as somethig i the middle, because we use a semiparametric approximatio ^Q. The iverse of ^Q provides a cosistet estimator of VaR for the distributio fuctio F. I this case, 8 >< dvar p ¼ >: u þ ^s ðmlþ iffx j F ðxþ 1 pg, ðu Þ p 1 F ðu Þ ^j ðmlþ ðu Þ ^j ðmlþ ðu Þ 1 1 p F ðu Þ, 1 p > F ðu Þ : ð43þ
17 Gozalo & Olmo Which Extreme Values are Really Extreme? 365 Whe the distributio fuctio is regularly varyig (j > 0), the tail of ^Q is modeled as a Pareto distributio ad the iverse of F is cosistetly estimated by 8 if fx j F ðxþ 1 pg, 1 p F ðu Þ >< dvar p ¼ 1 ^j F ðu Þ ðmlþ ðu >: u Þ, 1 p > F ðu Þ : ð44þ p The ucertaity of these estimates ca be measured by bootstrap cofidece p itervals, sice the exact fiite sample distributio fuctio of V ¼ ffiffiffi ð VaR d VaR p Þ is ot kow ad its asymptotic distributio depeds o uisace parameters. Let J ðx, ^Q Þ be the bootstrap approximatio of the exact distributio of V. A twosided, equaltailed cofidece iterval for VaR p, at a sigificace level a, is therefore give by CI a ðvar p Þ¼ VaR d p 1 pffiffiffi J 1 1 a 2, ^Q, VaR d p 1 pffiffiffi J 1 a 2, ^Q, ð45þ where J 1ð1 a, ^Q Þ is the 1 a bootstrap quatile. 4.1 Data Features The data we use to illustrate how the methodology proposed i this article ca be applied cosist of five fiacial idexes of major stock markets over the period December 19, to April 20, Frakfurt (Dax), Lodo (FTSE100), Madrid (Ibex), Tokyo (Nikkei), ad New York (Dow Joes). These data have bee collected from The observatios cosidered for the aalysis are the logarithmic returs measured i percetage terms ad deoted as r t : r t ¼ 100 ðlog P t log P t 1 Þ, where P t is the origial price at time t. For calculatig ease, the egative observatios (losses) are depicted i the positive tail. A first glace to the stadard statistic for kurtosis shows that most of these series are leptokurtic. For istace, the Dax idex has a coefficiet of corrected kurtosis of 5.70; FTSE, 1.34; Ibex, 3.88; Nikkei, 2.77, ad the Dow Joes has a coefficiet of Traditioally this measure has bee cosidered a idicator of heavy tails. Nevertheless, the coefficiet of kurtosis does ot provide us with adequate iformatio about the source of the heaviess. The tail idex, however, provides this kid of iformatio, focusig o a particular tail. For istace, j > 0 correspods to distributios where that tail has a polyomial decay [a more detailed discussio ca be foud i Shiryaev (2001)]. Table 5 presets oparametric bootstrap cofidece itervals for the tail idex [see Equatio (25)] obtaied by the differet approaches ivestigated throughout the article. From Table 5, it appears that the tail idex j is greater tha zero, idicatig the existece of heavy righthad side tails (correspodig to losses). The oly
18 366 Joural of Fiacial Ecoometrics Table 5 Bootstrap cofidece itervals for tail idex. ^j ðmlþ ðu ðwpþ Þ ^j ðhþ ðu ðwpþ Þ ^j ðpþ ðuðpþ Þ ^j ðmlþ ðx ð 95 Dax [ 0.02; 0.24; 0.84] [0.30; 0.31; 0.36] [ 0.50; 0.37; 0.20] [ 0.13; 0.22; 0.65] Ftse [ 0.57; 0.26; 0.04] [0.07; 0.11; 0.12] [ 0.44; 0.28; 0.08] [ 0.54; 0.29; 0.13] Ibex [ 0.12; 0.28; 0.87] [0.32; 0.37; 0.38] [ 0.43; 0.21; 0.04] [ 0.04; 0.46; 0.90] Nikkei [ 0.13; 0.11; 0.55] [0.33; 0.34; 0.39] [ 0.34; 0.19; 0.03 ] [ 0.25; 0.07; 0.50] Dow Joes [ 0.11; 0.63; 1.52] [0.33; 0.41; 0.44] [ 0.24; 0.22; 0.03] [0.05; 0.76; 1.72] 100 ÞÞ Bootstrap cofidece itervals (a ¼ 0.05) ad poitwise estimatio of the tail idex j for stock returs over the period December 19, to April 20, Bootstrap samples B ¼ Table 6 Bootstrap cofidece itervals for VaR. VaR GPD PD F Gaussia Dax [3.57; 4.16; 7.83] [3.48; 4.25; 4.93] [2.96; 4.33; 5.04] [3.52; 3.62; 3.71] Ftse [2.81; 3.04; 3.40] [2.83; 3.05; 3.31] [2.83; 3.08; 3.32] [2.65; 2.78; 2.85] Ibex [3.25; 3.92; 4.69] [2.94; 3.91; 4.62] [3.02; 4.50; 5.80] [3.08; 3.19; 3.32] Nikkei [3.69; 4.24; 8.30] [3.33; 4.31; 5.00] [4.09; 4.73; 5.95] [3.75; 3.79; 3.83] Dow Joes [1.47; 2.09; 2.60] [1.56; 2.09; 2.49] [1.36; 1.90; 2.15] [1.55; 1.73; 1.97] Cofidece itervals (a ¼ 0.05) ad poitwise estimatio of the VaR for the differet fiacial returs calculated with differet methodologies: our GPD ad PD approaches, oparametric approach F, ad a parametric approach based o a gaussia assumptio. The VaR idicates the percetage of retur losses with p ¼ 0.01 ad a holdig period of 1 day. The data covers the period December 19, 1994 to April 20, Bootstrap samples B ¼ exceptio is the Ftse idex, where there are some reasoable doubts. For that reaso, i the ext table the VaR is calculated uder both the GPD ad PD methodologies. I Table 6 we provide poitwise estimates ad cofidece itervals for VaR uder four differet approaches. The first two correspod to the methods developed i this article, ad the last two correspod to the stadard empirical methodologies that will be used here as a bechmark. From Table 6, three coclusios ca be obtaied: (i) Comparig our two approaches ad takig ito accout the results of the previous table, the PD method outperforms the GPD from a efficiecy poit of view, give that the poit estimates are very similar. This is the expected result uder the presece of heavy tails. (ii) The approach based o the empirical distributio is less efficiet compared to the PD method. The mai reaso is the lack of observatios comig from the tail, somethig that our PD method overcomes by properly parameterizig the tail. (iii) The approach based o gaussiaity, as expected, is very coservative i the sese of requirig a lesser amout of capital (smaller VaR).
19 Gozalo & Olmo Which Extreme Values are Really Extreme? CONCLUSION Risk ad ucertaity are ot the same thig [see Grager (2002)] ad therefore they eed to be characterized by differet measures. It is accepted that variace is well desiged to capture the latter, but ot the former. To measure risk, i other words, to respod to the questio if thigs go wrog, how wrog they ca go?, it is first ecessary to fid a aswer to the questio Which extreme values are really extreme? This is the mai goal of this article, where, followig Pickads (1975) methodology, we ot oly formally defie the set of extreme observatios of a particular sequece, but also, by meas of a hypothesis test, we defie the extreme values of ay sequece of the same legth ad with the same distributio fuctio. Idetificatio of the extreme observatios allows us to estimate risk measures such as VaR very accurately, as well as to make ifereces o differet tail parameters of iterest. Extesios to depedet data ad to multivariate extremes costitute curret research by the authors. APPENDIX Proof of Theorem 1 Let {U } be a sequece of idepedet ad idetically distributed (i.i.d.) uiform radom variables o [0, 1] ad let l be a parameter i p 0 < l < 1. Defie the empirical process B ðtþ ¼ ffiffiffi ðul; ðtþ U l ðtþþ with U l; ðtþ¼ 1fl<ti tg. This process has a biomial distributio Bi(, U l (t)). By 1 P i¼1 1 P j¼1 1 ft j >lg the Dosker theorem or empirical cetral limit theorem, B (t) coverges weakly to N(0, U l (t)(1 U l (t))), therefore the fiite dimesioal distributios are ormal for ay fixed t 2 [0,1]. I additio, the process is tight due to the uiform cotiuity of the distributio fuctio U ad of U l (t). This implies that B (t) coverges weakly to a meazero gaussia process Z Ul ðtþ. It oly remais to fid the asymptotic covariace fuctio, pffiffiffi pffiffiffi covðb ðsþ,b ðtþþ¼cov½ ðul; ðsþ U l ðsþþ, ðul; ðtþ U l ðtþþš, with 0 < s, t < 1. As U l (t) is costat give t 2 (0, 1), the covariace fuctio boils dow to! covðb ðsþ,b ðtþþ ¼ ð1 U ðlþþ 2 cov 1 X 1 fl<ti sg, 1 X 1 fl<ti tg : The observatios {t 1,..., t } are i.i.d., ad therefore covð1 fl<ti sg, 1 fl<tj tgþ ¼0 with i 6¼ j. The covariace fuctio is i this case 1 covðb ðsþ, B ðtþþ ¼ ð1 U ðlþþ 2 covð1 fl<t i sg,1 fl<ti tgþ ð46þ 1 ¼ ð1 U ðlþþ 2 ½Eð1 fl<t i miðs;tþgþ Eð1 fl<ti sgþeð1 fl<ti tgþš ðuðmiðs,tþþ UðlÞÞ ðuðsþ UðlÞÞðUðtÞ UðlÞÞ ¼ ð1 U ðlþþ 2, ð47þ i¼1 i¼1
20 368 Joural of Fiacial Ecoometrics with 0 < s, t < 1. Therefore B (t) coverges weakly to the distributio of a meazero gaussia process Z Ul ðtþ with covariace fuctio give by ðmiðs, tþ lþ ðs lþðt lþ covðz Ul ðsþ,z Ul ðtþþ ¼ ð1 lþ 2 : ð48þ For F cotiuous ad strictly icreasig, we ca defie u ¼ F 1 (l). Costruct x 1,..., x i.i.d. from F via x i ¼ F 1 (t i ) ad let F (x) deote the empirical distributio fuctio based o x 1,..., x. By the mootoicity of F, P i¼1 1 fu <x i xg ¼ P i¼1 1 ffðu Þ < Fðx i ÞFðxÞg ad therefore F u ;ðxþ defied i Equatio (19) satisfies F u ;ðxþ ¼U l; ðtþ with x ¼ F 1 (t). The the process B (t) becomes equal to the pffiffiffi process ðfu, ðyþ F u ðyþþ with y ¼ x u [see Equatios (19) ad (20)] ad the covariace fuctio is covðz Fu ðy 1 Þ,Z Fu ðy 2 ÞÞ ¼ ðfðmiðy 1,y 2 ÞÞ Fðu ÞÞ ðfðy 1 Þ Fðu ÞÞðFðy 2 Þ Fðu ÞÞ ð1 Fðu ÞÞ 2, with y 1 ¼ F 1 (s) ad y 2 ¼ F 1 (t). Uder the ull hypothesis F u ¼ GPD j;sðuþ, the empirical process S (y) pffiffiffi amouts to ðfu, ðyþ GPD j;sðu ÞðyÞÞ ad the covariace fuctio of the limitig process is covðz Fu ðy 1 Þ,Z Fu ðy 2 ÞÞ ¼ GPD j;sðmiðy 1,y 2 ÞÞ GPD j;s ðy 1 ÞGPD j;s ðy 2 Þ: ð50þ 1 Fðu Þ ð49þ Proof of Lemma 1 Let 0 < a < 1 be the sigificace level of the test ad cosider L(x,F) cotiuous ad strictly icreasig. By defiitio PfT > L 1 ð1 a,fþg ¼ a, with L 1 (1 a, F) the 1 a asymptotic quatile. Cosider L (x, Q ) the bootstrap approximatio of L (x; F) ad L 1 ð1 a,q Þ its 1 a quatile. Therefore if sup x2r j Q ðxþ FðxÞj! p 0, the L 1 ð1 a,q Þ! L 1 ð1 a,fþ with probability oe ad by Slutsky s theorem PfT > L 1 ð1 a,q Þg! PfT > L 1 ð1 a,fþg ¼ a: ð51þ & Received March 5, 2003; revised February 19, 2004; accepted April 21, 2004 REFERENCES Aderso, T. W., ad D. A. Darlig. (1952). Asymptotic Theory of Certai Goodess of Fit Criteria Based o Stochastic Processes. Aals of Mathematical Statistics 23, Bera, R. (1986). Simulated Power Fuctios. Aals of Statistics 14,
In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008
I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces
More informationI. Chisquared Distributions
1 M 358K Supplemet to Chapter 23: CHISQUARED DISTRIBUTIONS, TDISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad tdistributios, we first eed to look at aother family of distributios, the chisquared distributios.
More informationChapter 7 Methods of Finding Estimators
Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of
More information0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5
Sectio 13 KolmogorovSmirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.
More informationOutput Analysis (2, Chapters 10 &11 Law)
B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should
More informationKey Ideas Section 81: Overview hypothesis testing Hypothesis Hypothesis Test Section 82: Basics of Hypothesis Testing Null Hypothesis
Chapter 8 Key Ideas Hypothesis (Null ad Alterative), Hypothesis Test, Test Statistic, Pvalue Type I Error, Type II Error, Sigificace Level, Power Sectio 81: Overview Cofidece Itervals (Chapter 7) are
More informationDefinition. Definition. 72 Estimating a Population Proportion. Definition. Definition
7 stimatig a Populatio Proportio I this sectio we preset methods for usig a sample proportio to estimate the value of a populatio proportio. The sample proportio is the best poit estimate of the populatio
More informationAsymptotic Growth of Functions
CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll
More informationProperties of MLE: consistency, asymptotic normality. Fisher information.
Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout
More informationHypothesis testing. Null and alternative hypotheses
Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate
More informationCase Study. Normal and t Distributions. Density Plot. Normal Distributions
Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca
More informationConfidence Intervals for One Mean with Tolerance Probability
Chapter 421 Cofidece Itervals for Oe Mea with Tolerace Probability Itroductio This procedure calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) with
More information5: Introduction to Estimation
5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample
More information3. Covariance and Correlation
Virtual Laboratories > 3. Expected Value > 1 2 3 4 5 6 3. Covariace ad Correlatio Recall that by takig the expected value of various trasformatios of a radom variable, we ca measure may iterestig characteristics
More informationSequences and Series
CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their
More informationStatistical inference: example 1. Inferential Statistics
Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either
More information1 Introduction to reducing variance in Monte Carlo simulations
Copyright c 007 by Karl Sigma 1 Itroductio to reducig variace i Mote Carlo simulatios 11 Review of cofidece itervals for estimatig a mea I statistics, we estimate a uow mea µ = E(X) of a distributio by
More information1. C. The formula for the confidence interval for a population mean is: x t, which was
s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : pvalue
More informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationORDERS OF GROWTH KEITH CONRAD
ORDERS OF GROWTH KEITH CONRAD Itroductio Gaiig a ituitive feel for the relative growth of fuctios is importat if you really wat to uderstad their behavior It also helps you better grasp topics i calculus
More informationChapter 7  Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:
Chapter 7  Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries
More information9.8: THE POWER OF A TEST
9.8: The Power of a Test CD91 9.8: THE POWER OF A TEST I the iitial discussio of statistical hypothesis testig, the two types of risks that are take whe decisios are made about populatio parameters based
More information7. Sample Covariance and Correlation
1 of 8 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 7. Sample Covariace ad Correlatio The Bivariate Model Suppose agai that we have a basic radom experimet, ad that X ad Y
More informationModified Line Search Method for Global Optimization
Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o
More informationHypothesis Tests Applied to Means
The Samplig Distributio of the Mea Hypothesis Tests Applied to Meas Recall that the samplig distributio of the mea is the distributio of sample meas that would be obtaied from a particular populatio (with
More informationConfidence Intervals for the Mean of Nonnormal Data Class 23, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom
Cofidece Itervals for the Mea of Noormal Data Class 23, 8.05, Sprig 204 Jeremy Orloff ad Joatha Bloom Learig Goals. Be able to derive the formula for coservative ormal cofidece itervals for the proportio
More informationDetermining the sample size
Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors
More informationEconomics 140A Confidence Intervals and Hypothesis Testing
Ecoomics 140A Cofidece Itervals ad Hypothesis Testig Obtaiig a estimate of a parameter is ot the al purpose of statistical iferece because it is highly ulikely that the populatio value of a parameter is
More informationSection 73 Estimating a Population. Requirements
Sectio 73 Estimatig a Populatio Mea: σ Kow Key Cocept This sectio presets methods for usig sample data to fid a poit estimate ad cofidece iterval estimate of a populatio mea. A key requiremet i this sectio
More informationInference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval
Chapter 8 Tests of Statistical Hypotheses 8. Tests about Proportios HT  Iferece o Proportio Parameter: Populatio Proportio p (or π) (Percetage of people has o health isurace) x Statistic: Sample Proportio
More informationwhen n = 1, 2, 3, 4, 5, 6, This list represents the amount of dollars you have after n days. Note: The use of is read as and so on.
Geometric eries Before we defie what is meat by a series, we eed to itroduce a related topic, that of sequeces. Formally, a sequece is a fuctio that computes a ordered list. uppose that o day 1, you have
More informationPSYCHOLOGICAL STATISTICS
UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION B Sc. Cousellig Psychology (0 Adm.) IV SEMESTER COMPLEMENTARY COURSE PSYCHOLOGICAL STATISTICS QUESTION BANK. Iferetial statistics is the brach of statistics
More informationSAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx
SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval
More informationSequences II. Chapter 3. 3.1 Convergent Sequences
Chapter 3 Sequeces II 3. Coverget Sequeces Plot a graph of the sequece a ) = 2, 3 2, 4 3, 5 + 4,...,,... To what limit do you thik this sequece teds? What ca you say about the sequece a )? For ǫ = 0.,
More informationInfinite Sequences and Series
CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...
More informationNotes on Hypothesis Testing
Probability & Statistics Grishpa Notes o Hypothesis Testig A radom sample X = X 1,..., X is observed, with joit pmf/pdf f θ x 1,..., x. The values x = x 1,..., x of X lie i some sample space X. The parameter
More informationChapter 10. Hypothesis Tests Regarding a Parameter. 10.1 The Language of Hypothesis Testing
Chapter 10 Hypothesis Tests Regardig a Parameter A secod type of statistical iferece is hypothesis testig. Here, rather tha use either a poit (or iterval) estimate from a simple radom sample to approximate
More informationPROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUSMALUS SYSTEM
PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUSMALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics
More informationModule 4: Mathematical Induction
Module 4: Mathematical Iductio Theme 1: Priciple of Mathematical Iductio Mathematical iductio is used to prove statemets about atural umbers. As studets may remember, we ca write such a statemet as a predicate
More informationNonlife insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring
Nolife isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy
More informationChapter 6: Variance, the law of large numbers and the MonteCarlo method
Chapter 6: Variace, the law of large umbers ad the MoteCarlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value
More informationZTEST / ZSTATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown
ZTEST / ZSTATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large TTEST / TSTATISTIC: used to test hypotheses about
More informationLecture Notes CMSC 251
We have this messy summatio to solve though First observe that the value remais costat throughout the sum, ad so we ca pull it out frot Also ote that we ca write 3 i / i ad (3/) i T () = log 3 (log ) 1
More information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More informationA Brief Study about Nonparametric Adherence Tests
A Brief Study about Noparametric Adherece Tests Viicius R. Domigues, Lua C. S. M. Ozelim Abstract The statistical study has become idispesable for various fields of kowledge. Not ay differet, i Geotechics
More informationNPTEL STRUCTURAL RELIABILITY
NPTEL Course O STRUCTURAL RELIABILITY Module # 0 Lecture 1 Course Format: Web Istructor: Dr. Aruasis Chakraborty Departmet of Civil Egieerig Idia Istitute of Techology Guwahati 1. Lecture 01: Basic Statistics
More informationIncremental calculation of weighted mean and variance
Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically
More informationLecture 10: Hypothesis testing and confidence intervals
Eco 514: Probability ad Statistics Lecture 10: Hypothesis testig ad cofidece itervals Types of reasoig Deductive reasoig: Start with statemets that are assumed to be true ad use rules of logic to esure
More informationData Analysis and Statistical Behaviors of Stock Market Fluctuations
44 JOURNAL OF COMPUTERS, VOL. 3, NO. 0, OCTOBER 2008 Data Aalysis ad Statistical Behaviors of Stock Market Fluctuatios Ju Wag Departmet of Mathematics, Beijig Jiaotog Uiversity, Beijig 00044, Chia Email:
More informationTIEE Teaching Issues and Experiments in Ecology  Volume 1, January 2004
TIEE Teachig Issues ad Experimets i Ecology  Volume 1, Jauary 2004 EXPERIMENTS Evirometal Correlates of Leaf Stomata Desity Bruce W. Grat ad Itzick Vatick Biology, Wideer Uiversity, Chester PA, 19013
More informationConfidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
More informationOverview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals
Overview Estimatig the Value of a Parameter Usig Cofidece Itervals We apply the results about the sample mea the problem of estimatio Estimatio is the process of usig sample data estimate the value of
More informationA probabilistic proof of a binomial identity
A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two
More informationInstitute for the Advancement of University Learning & Department of Statistics
Istitute for the Advacemet of Uiversity Learig & Departmet of Statistics Descriptive Statistics for Research (Hilary Term, 00) Lecture 5: Cofidece Itervals (I.) Itroductio Cofidece itervals (or regios)
More information1 Hypothesis testing for a single mean
BST 140.65 Hypothesis Testig Review otes 1 Hypothesis testig for a sigle mea 1. The ull, or status quo, hypothesis is labeled H 0, the alterative H a or H 1 or H.... A type I error occurs whe we falsely
More informationAQA STATISTICS 1 REVISION NOTES
AQA STATISTICS 1 REVISION NOTES AVERAGES AND MEASURES OF SPREAD www.mathsbox.org.uk Mode : the most commo or most popular data value the oly average that ca be used for qualitative data ot suitable if
More informationUnit 20 Hypotheses Testing
Uit 2 Hypotheses Testig Objectives: To uderstad how to formulate a ull hypothesis ad a alterative hypothesis about a populatio proportio, ad how to choose a sigificace level To uderstad how to collect
More informationSoving Recurrence Relations
Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree
More informationMeasures of Spread and Boxplots Discrete Math, Section 9.4
Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,
More informationBASIC STATISTICS. Discrete. Mass Probability Function: P(X=x i ) Only one finite set of values is considered {x 1, x 2,...} Prob. t = 1.
BASIC STATISTICS 1.) Basic Cocepts: Statistics: is a sciece that aalyzes iformatio variables (for istace, populatio age, height of a basketball team, the temperatures of summer moths, etc.) ad attempts
More information4.1 Sigma Notation and Riemann Sums
0 the itegral. Sigma Notatio ad Riema Sums Oe strategy for calculatig the area of a regio is to cut the regio ito simple shapes, calculate the area of each simple shape, ad the add these smaller areas
More informationPlugin martingales for testing exchangeability online
Plugi martigales for testig exchageability olie Valetia Fedorova, Alex Gammerma, Ilia Nouretdiov, ad Vladimir Vovk Computer Learig Research Cetre Royal Holloway, Uiversity of Lodo, UK {valetia,ilia,alex,vovk}@cs.rhul.ac.uk
More informationUniversity of California, Los Angeles Department of Statistics. Distributions related to the normal distribution
Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chisquare (χ ) distributio.
More informationON SMALL SAMPLE PROPERTIES OF PERMUTATION TESTS: A SIGNIFICANCE TEST FOR REGRESSION MODELS*
Kobe Uiversity Ecoomic Review 52 (2006) 27 ON SMALL SAMPLE PROPERTIES OF PERMUTATION TESTS: A SIGNIFICANCE TEST FOR REGRESSION MODELS* By HISASHI TANIZAKI I this paper, we cosider a oparametric permutatio
More informationTheorems About Power Series
Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real oegative umber R, called the radius
More informationIrreducible polynomials with consecutive zero coefficients
Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem
More informationCenter, Spread, and Shape in Inference: Claims, Caveats, and Insights
Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the
More informationLesson 12. Sequences and Series
Retur to List of Lessos Lesso. Sequeces ad Series A ifiite sequece { a, a, a,... a,...} ca be thought of as a list of umbers writte i defiite order ad certai patter. It is usually deoted by { a } =, or
More information8.5 Alternating infinite series
65 8.5 Alteratig ifiite series I the previous two sectios we cosidered oly series with positive terms. I this sectio we cosider series with both positive ad egative terms which alterate: positive, egative,
More informationStat 104 Lecture 16. Statistics 104 Lecture 16 (IPS 6.1) Confidence intervals  the general concept
Statistics 104 Lecture 16 (IPS 6.1) Outlie for today Cofidece itervals Cofidece itervals for a mea, µ (kow σ) Cofidece itervals for a proportio, p Margi of error ad sample size Review of mai topics for
More informationStandard Errors and Confidence Intervals
Stadard Errors ad Cofidece Itervals Itroductio I the documet Data Descriptio, Populatios ad the Normal Distributio a sample had bee obtaied from the populatio of heights of 5yearold boys. If we assume
More informationLecture 4: Cauchy sequences, BolzanoWeierstrass, and the Squeeze theorem
Lecture 4: Cauchy sequeces, BolzaoWeierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits
More informationThe following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles
The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio
More informationARTICLE IN PRESS. Statistics & Probability Letters ( ) A Kolmogorovtype test for monotonicity of regression. Cecile Durot
STAPRO 66 pp:  col.fig.: il ED: MG PROD. TYPE: COM PAGN: Usha.N  SCAN: il Statistics & Probability Letters 2 2 2 2 Abstract A Kolmogorovtype test for mootoicity of regressio Cecile Durot Laboratoire
More informationCHAPTER 3 THE TIME VALUE OF MONEY
CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all
More informationSection IV.5: Recurrence Relations from Algorithms
Sectio IV.5: Recurrece Relatios from Algorithms Give a recursive algorithm with iput size, we wish to fid a Θ (best big O) estimate for its ru time T() either by obtaiig a explicit formula for T() or by
More informationA CHARACTERIZATION OF MINIMAL ZEROSEQUENCES OF INDEX ONE IN FINITE CYCLIC GROUPS
INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 5(1) (2005), #A27 A CHARACTERIZATION OF MINIMAL ZEROSEQUENCES OF INDEX ONE IN FINITE CYCLIC GROUPS Scott T. Chapma 1 Triity Uiversity, Departmet
More informationINVESTMENT PERFORMANCE COUNCIL (IPC)
INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks
More informationDepartment of Computer Science, University of Otago
Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS200609 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly
More informationClass Meeting # 16: The Fourier Transform on R n
MATH 18.152 COUSE NOTES  CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,
More informationTAYLOR SERIES, POWER SERIES
TAYLOR SERIES, POWER SERIES The followig represets a (icomplete) collectio of thigs that we covered o the subject of Taylor series ad power series. Warig. Be prepared to prove ay of these thigs durig the
More informationINFINITE SERIES KEITH CONRAD
INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal
More informationAdvanced Probability Theory
Advaced Probability Theory Math5411 HKUST Kai Che (Istructor) Chapter 1. Law of Large Numbers 1.1. σalgebra, measure, probability space ad radom variables. This sectio lays the ecessary rigorous foudatio
More informationSection 11.3: The Integral Test
Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult
More informationChapter 7: Confidence Interval and Sample Size
Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum
More information{{1}, {2, 4}, {3}} {{1, 3, 4}, {2}} {{1}, {2}, {3, 4}} 5.4 Stirling Numbers
. Stirlig Numbers Whe coutig various types of fuctios from., we quicly discovered that eumeratig the umber of oto fuctios was a difficult problem. For a domai of five elemets ad a rage of four elemets,
More informationConvexity, Inequalities, and Norms
Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for
More informationDivide and Conquer. Maximum/minimum. Integer Multiplication. CS125 Lecture 4 Fall 2015
CS125 Lecture 4 Fall 2015 Divide ad Coquer We have see oe geeral paradigm for fidig algorithms: the greedy approach. We ow cosider aother geeral paradigm, kow as divide ad coquer. We have already see a
More informationDiscrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13
EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may
More informationMeasurable Functions
Measurable Fuctios Dug Le 1 1 Defiitio It is ecessary to determie the class of fuctios that will be cosidered for the Lebesgue itegratio. We wat to guaratee that the sets which arise whe workig with these
More informationarxiv:1506.03481v1 [stat.me] 10 Jun 2015
BEHAVIOUR OF ABC FOR BIG DATA By Wetao Li ad Paul Fearhead Lacaster Uiversity arxiv:1506.03481v1 [stat.me] 10 Ju 2015 May statistical applicatios ivolve models that it is difficult to evaluate the likelihood,
More informationStatistics Lecture 14. Introduction to Inference. Administrative Notes. Hypothesis Tests. Last Class: Confidence Intervals
Statistics 111  Lecture 14 Itroductio to Iferece Hypothesis Tests Admiistrative Notes Sprig Break! No lectures o Tuesday, March 8 th ad Thursday March 10 th Exteded Sprig Break! There is o Stat 111 recitatio
More informationHypothesis testing in a Nutshell
Hypothesis testig i a Nutshell Summary by Pamela Peterso Drake Itroductio The purpose of this readig is to discuss aother aspect of statistical iferece, testig. A is a statemet about the value of a populatio
More informationCase Study. Contingency Tables. Graphing Tabled Counts. Stacked Bar Graph
Case Study Cotigecy Tables Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 4 6, 2011 Case Study Example 9.3 begiig o page 213 of the text describes a experimet i which
More informationEstimating the Mean and Variance of a Normal Distribution
Estimatig the Mea ad Variace of a Normal Distributio Learig Objectives After completig this module, the studet will be able to eplai the value of repeatig eperimets eplai the role of the law of large umbers
More informationNormal Distribution.
Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued
More informationB1. Fourier Analysis of Discrete Time Signals
B. Fourier Aalysis of Discrete Time Sigals Objectives Itroduce discrete time periodic sigals Defie the Discrete Fourier Series (DFS) expasio of periodic sigals Defie the Discrete Fourier Trasform (DFT)
More informationThe analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection
The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity
More informationLecture 7: Borel Sets and Lebesgue Measure
EE50: Probability Foudatios for Electrical Egieers JulyNovember 205 Lecture 7: Borel Sets ad Lebesgue Measure Lecturer: Dr. Krisha Jagaatha Scribes: Ravi Kolla, Aseem Sharma, Vishakh Hegde I this lecture,
More information