Which Extreme Values Are Really Extreme?

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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 Kolmogorov-Smirov 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, goodess-of-fit 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 so-called 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 so-called 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 maximum-likelihood 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 maximum-likelihood estimator for the parameter j ad s(u ) of the GPD. I this case there is ot a closed expressio for the maximum-likelihood estimators of these parameters, ad we have to rely o umerical procedures [see Press (1992)]. The maximum-likelihood 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 mea-squared 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 Kolmogorov-Smirov (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 maximum-likelihood 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 mea-squared 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 t-distributios, 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 heavy-tailed 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 heavy-tailed 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 goodess-of-fit 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 goodess-of-fit test statistic is the KS statistic [for other goodess-offit 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 mea-zero 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 mea-zero 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 mea-zero 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 maximum-likelihood 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 maximum-likelihood 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 maximum-likelihood 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 p-value replaces the rejectio criteria give i Equatio (40). The empirical p-value 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 p-value 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 t-distributio 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 by-product of this table is that our test ca be cosidered a goodess-of-fit 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 t-distributio fuctios, with u from d WP. F 0 deotes the data-geeratig 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 t-distributio fuctios, with differet ad hoc threshold choices for a sample size ¼ F 0 deotes the data-geeratig 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 two-sided, equal-tailed 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 (FTSE-100), 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 right-had 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 mea-zero 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,

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