Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers


 Phillip Park
 1 years ago
 Views:
Transcription
1 Forthcomin, International Journal of Forecastin Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers Francis X. Diebold University of Pennsylvania and NBER Kamil Yilmaz Koç University, Istanbul First draft/print: November 8 This draft/print: March Abstract: Usin a eneralized vector autoreressive framework in which forecasterror variance decompositions are invariant to variable orderin, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, forein exchane and commodities markets, from January 999 throuh January. We show that despite sinificant volatility fluctuations in all four markets durin the sample, crossmarket volatility spillovers were quite limited until the lobal financial crisis that bean in 7. As the crisis intensified so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets takin place after the collapse of Lehman Brothers in September 8. JEL classification numbers: G, F3 Keywords: Asset Market, Asset Return, Stock Market, Market Linkae, Financial Crisis, Contaion, Vector Autoreression, Variance Decomposition Acknowledements: For helpful comments we thank two anonymous referees and participants in the Cemapre / IIF International Workshop on the Predictability of Financial Markets, especially Nuno Crato, Antonio Espasa, Antonio GarciaFerrer, Raquel Gaspar, and Esther Ruiz. All errors, however, are entirely ours. We thank the National Science Foundation for financial support.
2 . Introduction Financial crises occur with notable reularity, and moreover, they display notable similarities (e.., Reinhart and Rooff, 8). Durin crises, for example, financial market volatility enerally increases sharply and spills over across markets. One would naturally like to be able to measure and monitor such spillovers, both to provide early warnin systems for emerent crises, and to track the proress of extant crises. Motivated by such considerations, Diebold and Yilmaz (DY, 9) introduce a volatility spillover measure based on forecast error variance decompositions from vector autoreressions (VARs). It can be used to measure spillovers in returns or return volatilities (or, for that matter, any return characteristic of interest) across individual assets, asset portfolios, asset markets, etc., both within and across countries, revealin spillover trends, cycles, bursts, etc. In addition, althouh it conveys useful information, it nevertheless sidesteps the contentious issues associated with definition and existence of episodes of contaion or herd behavior. However, the DY framework as presently developed and implemented has several limitations, both methodoloical and substantive. Consider the methodoloical side. First, DY relies on Choleskyfactor identification of VARs, so the resultin variance decompositions can be dependent on variable orderin. One would prefer a spillover measure invariant to orderin. Second, and crucially, DY addresses only total spillovers VAR variance decompositions, introduced by Sims (98), record how much of the Hstepahead forecast error variance of some variable, i, is due to innovations in another variable, j. On contaion (or lack thereof) see, for example, Forbes and Riobon ().
3 (from/to each market i, to/from all other markets, added across i). One would also like to examine directional spillovers (from/to a particular market). Now consider the substantive side. DY considers only the measurement of spillovers across identical assets (equities) in different countries. But various other possibilities are also of interest, includin individualasset spillovers within countries (e.., amon the thirty Dow Jones Industrials in the U.S.), across asset classes (e.., between stock and bond markets in the U.S.), and of course various blends. Spillovers across asset classes, in particular, are of key interest iven the recent lobal financial crisis (which appears to have started in credit markets but spilled over into equities), but they have not yet been investiated in the DY framework. In this paper we fill these methodoloical and substantive aps. We use a eneralized vector autoreressive framework in which forecasterror variance decompositions are invariant to variable orderin, and we explicitly include directional volatility spillovers. We then use our methods in a substantive empirical analysis of daily volatility spillovers across U.S. stock, bond, forein exchane and commodities markets over a ten year period, includin the recent lobal financial crisis. We proceed as follows. In section we discuss our methodoloical approach, emphasizin in particular our new use of eneralized variance decompositions and directional spillovers. In section 3 we describe our data and present our substantive results. We conclude in section 4.
4 . Methods: Generalized Spillover Definition and Measurement Here we extend the DY spillover index, which follows directly from the familiar notion of a variance decomposition associated with an Nvariable vector autoreression. Whereas DY focuses on total spillovers in a simple VAR framework (i.e., with potentially orderdependent results driven by Cholesky factor orthoonalization), we proress by measurin directional spillovers in a eneralized VAR framework that eliminates the possible dependence of results on orderin. Consider a covariance stationary Nvariable VAR(p), p x = Φ x + ε, where t i t i t i= ε (, Σ) is a vector of independently and identically distributed disturbances. The movin averae representation is x = Aε, where the NxN coefficient matrices A i obey the t i t i i= recursion A =Φ A +Φ A Φ A, with A an NxN identity matrix and A i = for i i i p i p i<. The movin averae coefficients (or transformations such as impulseresponse functions or variance decompositions) are the key to understandin the dynamics of the system. We rely on variance decompositions, which allow us to parse the forecast error variances of each variable into parts attributable to the various system shocks. Variance decompositions allow us to assess the fraction of the Hstepahead error variance in forecastin x i that is due to shocks to x, j i, for each i. j Calculation of variance decompositions requires orthoonal innovations, whereas our VAR innovations are enerally contemporaneously correlated. Identification schemes such as that based on Cholesky factorization achieve orthoonality, but the variance decompositions then depend on orderin of the variables. We circumvent this problem by 3
5 exploitin the eneralized VAR framework of Koop, Pesaran and Potter (996) and Pesaran and Shin (998), hereafter KPPS, which produces variance decompositions invariant to orderin. Instead of attemptin to orthoonalize shocks, the eneralized approach allows correlated shocks but accounts for them appropriately usin the historically observed distribution of the errors. As the shocks to each variable are not orthoonalized, the sum of contributions to the variance of forecast error (that is, the row sum of the elements of the variance decomposition table) is not necessarily equal to one. Variance Shares Let us define own variance shares to be the fractions of the Hstepahead error variances in forecastin x i due to shocks to x i, for i=,,..,n, and cross variance shares, or spillovers, to be the fractions of the Hstepahead error variances in forecastin x i due to shocks to x j, for i, j =,,.., N, such that i j. Denotin the KPPS Hstepahead forecast error variance decompositions by θ ( H), for H =,,..., we have ij σ θ ( H ) = ij H ' ii ( ea ) i hσe h= j H ' ' ( ea ) h i hσae = h i () where Σ is the variance matrix for the error vector ε, σ ii is the standard deviation of the error term for the ith equation and ei is the selection vector with one as the ith element and zeros otherwise. As explained above, the sum of the elements of each row of the variance decomposition table is not equal to : N j= θ ( H ). In order to use the information ij 4
6 available in the variance decomposition matrix in the calculation of the spillover index, we normalize each entry of the variance decomposition matrix by the row sum as 3 : θij ( H ) θij ( H) = N () θ ( H) j= ij Note that, by construction, N θij ( H ) = and j= N θij ( H ) = N. i, j= Total Spillovers Usin the volatility contributions from the KPPS variance decomposition, we can construct a total volatility spillover index: S N N θij H ij i, j= i, j= i j i j i N θij ( H ) i, j= ( ) θ ( H) ( H) = = i. (3) N This is the KPPS analo of the Cholesky factor based measure used in Diebold and Yilmaz (9). The total spillover index measures the contribution of spillovers of volatility shocks across four asset classes to the total forecast error variance. Directional Spillovers Althouh it is sufficient to study the total volatility spillover index to understand how much of shocks to volatility spill over across major asset classes, the eneralized VAR approach enables us to learn about the direction of volatility spillovers across major asset 3 Alternatively, we can normalize the elements of the variance decomposition matrix with the column sum of these elements and compare the resultin total spillover index with the one obtained from the normalization with the row sum. 5
7 classes. As the eneralized impulse responses and variance decompositions are invariant to the orderin of variables, we calculate the directional spillovers usin the normalized elements of the eneralized variance decomposition matrix. We measure directional volatility spillovers received by market i from all other markets j as: S ii N j= j i N j= θ ( H ) ij ( H) = i θ ( H ) ij (4) In similar fashion we measure directional volatility spillovers transmitted by market i to all other markets j as: S ii N j= j i N j= θ ( H ) ( H) = i θ ( H ) ji ji (5) One can think of the set of directional spillovers as providin a decomposition of total spillovers into those comin from (or to) a particular source. Net Spillovers We obtain the net volatility spillover from market i to all other markets j as S ( H) = S ( H) S ( H) i ii ii (6) The net volatility spillover is simply the difference between ross volatility shocks transmitted to and ross volatility shocks received from all other markets. Net Pairwise Spillovers 6
8 The net volatility spillover (6) provides summary information about how much in net terms each market contributes to volatility in other markets. It is also of interest to examine net pairwise volatility spillovers, which we define as: S θ ( H) θ ( H) ( ) i ( ) ( H) ij ji ij H = N N θik H θ jk k= k= (7) The net pairwise volatility spillover between markets i and j is simply the difference between ross volatility shocks transmitted from market i to j and ross volatility shocks transmitted from j to i. 3. Empirics: Estimates of Volatility Spillovers across U.S. Asset Markets Here we use our framework to measure volatility spillovers amon four key U.S. asset classes: stocks, bonds, forein exchane and commodities. This is of particular interest because spillovers across asset classes may be an important aspect of the lobal financial crisis that bean in 7. In the remainder of this section we proceed as follows. We bein by describin our data in section 3a. Then we calculate averae (i.e., total) spillovers in section 3b. We then quantify spillover dynamics, examinin rollinsample total spillovers, rollinsample directional spillovers, rollinsample net directional spillovers and rollinsample net pairwise spillovers below. Stock, Bond, Exchane Rate, and Commodity Market Volatility Data We examine daily volatilities of returns on U.S. stock, bond, forein exchane, and commodity markets. In particular, we examine the S&P 5 index, the year Treasury 7
9 bond yield, the New York Board of Trade U.S. dollar index futures, and the DowJones/UBS commodity index. 4 The data span January 5, 999 throuh January 9,, for a total of 77 daily observations. In the tradition of a lare literature datin at least to Parkinson (98), we estimate daily variance usin daily hih and low prices. 5 For market i on day t we have max min σ it =.36 ln( Pit ) ln( Pit ), where min P is the maximum (hih) price in market i on day t, and P is the daily minimum max it it (low) price. Because σ is an estimator of the daily variance, the correspondin estimate of it the annualized daily percent standard deviation (volatility) is ˆ σ = 365 σ. We plot the four markets volatilities in Fiure and we provide summary statistics of lo volatility in Table. Several interestin facts emere, includin: () The bond and stock markets have been the most volatile (rouhly equally so), with commodity and FX markets comparatively less volatile, () volatility dynamics appear hihly persistent, in keepin with a lare literature summarized for example in Andersen, Bollerslev, Christoffersen and Diebold (6), and (3) all volatilities are hih durin the recent crisis, with stock and bond market volatility, in particular, displayin hue jumps. Throuhout the sample, stock market went throuh two periods of major volatility. In 999, daily stock market volatility was close to 5 percent, but it increased sinificantly to above 5 percent and fluctuated around that level until mid3, movin occasionally above 5 percent. After mid3, it declined to less than 5 percent and stayed there until Auust it it 4 The DJ/AIG commodity index was rebranded as the DJ/UBS commodity index followin the acquisition of AIG Financial Products Corp. by UBS Securities LLC on May 6, 9. 5 For backround, see Alizadeh, Brandt and Diebold () and the references therein. 8
10 7. Since Auust 7, stock market volatility reflects the dynamics of the subprime crisis quite well. In the first half of our sample, the interest rate volatility measured by the annualized standard deviation was comparable to the stock market volatility. While it was lower than 5 percent mark for most of, in the first and last few months of, it increased and fluctuated between 55 percent. Bond market volatility remained hih until mid5, and fell below 5 percent from late 5 throuh the first half of 7. Since Auust 7, volatility in bond markets has also increased sinificantly. Commodity market volatility used to be very low compared to stock and bond markets, but it increased slihtly over time and especially in 56 and recently in 8. FX market volatility has been the lowest amon the four markets. It increased in 8 and moved to a 55 percent band followin the collapse of Lehman Brothers in September 8. Since then, FX market volatility declined, but it is still above its averae for the last decade. Unconditional Patterns: The FullSample Volatility Spillover Table We call Table as volatility spillover table. Its th ij entry is the estimated contribution to the forecast error variance of market i comin from innovations to market j. 6 Hence the offdiaonal column sums (labeled contributions to others) or row sums (labeled contributions from others), are the to and from directional spillovers, and the from 6 All results are based on vector autoreressions of order 4 and eneralized variance decompositions of dayahead volatility forecast errors. To check for the sensitivity of the results to the choice of the order of VAR we calculate the spillover index for orders throuh 6, and plot the minimum, the maximum and the median values obtained in Fiure A of the Appendix. Similarly, we calculated the spillover index for forecast horizons varyin from 4 days to days. Both Fiure A and Fiure A of the Appendix show that the total spillover plot is not sensitive to the choice of the order of VAR or to the choice of the forecast horizon. 9
11 minus to differences are the net volatility spillovers. In addition, the total volatility spillover index appears in the lower riht corner of the spillover table. It is approximately the rand offdiaonal column sum (or row sum) relative to the rand column sum includin diaonals (or row sum includin diaonals), expressed as a percent. 7 The volatility spillover table provides an approximate inputoutput decomposition of the total volatility spillover index. Consider first what we learn from the table about directional spillovers (ross and net). From the directional to others row, we see that ross directional volatility spillovers to others from each of the four markets are not very different. We also see from the directional from others column that ross directional volatility spillovers from others to the bond market is relatively lare, at 8.5 percent, followed by the FX market with the spillovers from others explainin 4. percent of the forecast error variance. As for net directional volatility spillovers, the larest are from the stock market to others (6.9.4=5.5 percent) and from others to the FX market (.44.4=.8 percent). Now consider the total (nondirectional) volatility spillover, which is effectively a distillation of the various directional volatility spillovers into a sinle index. The total volatility spillover appears in the lower riht corner of Table, which indicates that on averae, across our entire sample,.6 percent of volatility forecast error variance in all four markets comes from spillovers. The summary of Table is simple: Both total and directional spillovers over the full sample period were quite low. 7 As we have already discussed in Section in detail, the approximate nature of the claim stems from the properties of the eneralized variance decomposition. With Cholesky factor identification the claim is exact rather than approximate; see also Diebold and Yilmaz (9).
12 Conditionin and Dynamics I: The RollinSample Total Volatility Spillover Plot Clearly, many chanes took place durin the years in our sample, January 999 January. Some are welldescribed as moreorless continuous evolution, such as increased linkaes amon lobal financial markets and increased mobility of capital, due to lobalization, the move to electronic tradin, and the rise of hede funds. Others, however, may be better described as bursts that subsequently subside. Given this backround of financial market evolution and turbulence, it seems unlikely that any sinle fixedparameter model would apply over the entire sample. Hence the fullsample spillover table and spillover index constructed earlier, althouh providin a useful summary of averae volatility spillover behavior, likely miss potentially important secular and cyclical movements in spillovers. To address this issue, we now estimate volatility spillovers usin day rollin samples, and we assess the extent and the nature of spillover variation over time via the correspondin time series of spillover indices, which we examine raphically in the socalled total spillover plot of Fiure. Startin at a value slihtly lower than 5 percent in the first window, the total volatility spillover plot for most of the time fluctuates between ten and twenty percent. However, there are important exceptions: The spillovers exceed the twenty percent mark in mid6 and most importantly by far exceed the thirty percent level, durin the lobal financial crisis of 79. We can identify several cycles in the total spillover plot. The first cycle started with the burst of the tech bubble in and the index climbed from 3 percent to percent. In the second half of the index increased to percent aain, before droppin back to percent at the end of January. After hittin the bottom in mid, the index went
13 throuh three relatively small cycles until the end of 5. The first cycle started in mid and lasted until the last quarter of 3. The second cycle was shorter, startin in the first quarter of 4 and endin in the third quarter. The third cycle durin this period starts in the middle of 4 and lasts until the end of 5. All three cycles involve movements of the index between and 5 percent. After the rather calm era from 3 throuh 5, the spillover index recorded a sinificant upward move in May throuh the end of 6. On May 9 th 6 the Federal Open Market Committee of the Federal Reserve decided to increase the federal funds taret rate from 4.75 percent to 5. percent and sinaled the likelihood of another increase in its June meetin. 8 After this decision the total spillover index increased from percent at the end of April 6 to 4 percent by November 6. The fact that FED was continuin to tihten the monetary policy led to an increase in volatility in the bond and FX markets which spilled over to other markets. Finally, the most interestin part of the total spillover plot concerns the recent financial crisis. One can see four volatility waves durin the recent crisis: JulyAuust 7 (credit crunch), JanuaryMarch 8 (panic in stock and forein exchane markets followed by an unscheduled rate cut of threequarters of a percentae points by Federal Reserve and Bear Stearns takeover by JP Moran in March), SeptemberDecember 8 (followin the collapse of Lehman Brothers) and in the first half of 9 as the financial crisis started to have its real effects around the world. Durin the JanuaryMarch 8 episode, and especially followin the collapse of Lehman Brothers in midseptember and consistent with 8 Indeed, the FOMC increased the federal funds taret rate to 5.5 percent in its June meetin and kept it at that level for more than a year until its September 7 meetin.
14 an unprecedented evaporation of liquidity worldwide, the spillover index sures above thirty percent. Conditionin and Dynamics II: RollinSample Gross Directional Volatility Spillover Plots Thus far we have discussed the total spillover plot, which is of interest but discards directional information. That information is contained in the Directional TO Others row (the sum of which is iven by S ( H) ii in equation 4) and the Directional FROM Others column (the sum of which is iven by Si i ( H) in equation 5). We now estimate the abovementioned row and column of Table dynamically, in a fashion precisely parallel to the earlierdiscussed total spillover plot. We call these directional spillover plots. In Fiure 3, we present the directional volatility spillovers from each of the four asset classes to others (corresponds to the directional to others row in Table ). They vary reatly over time. Durin tranquil times, spillovers from each market are below five percent, but durin volatile times, directional spillovers increase close to percent. Amon the four markets, ross volatility spillovers from the commodity markets to others are in enerally smaller than the spillovers from the other three markets. In Fiure 4, we present directional volatility spillovers from others to each of the four asset classes (corresponds to the directional from others column in Table ). As with the directional spillovers to others, the spillovers from others vary noticeably over time. The relative variation pattern, however, is reversed, with directional volatility spillovers to commodities and FX increasin relatively more in turbulent times. 3
15 Conditionin and Dynamics III: RollinSample Net Directional Volatility Spillover Plots Above we briefly discussed the ross spillover plots, because our main focus point is the net directional spillover plot presented in Fiure 5. Each point in Fiure 5a throuh 5d corresponds S ( H ) (equation 6) and is the difference between the Contribution from i column sum and the Contribution to row sum. In addition, as we described briefly at the end of section, we also calculate net pairwise spillovers between two markets (equation 7) and present these plots in Fiure 6. Until the recent lobal financial crisis net volatility spillovers from/to each of the four markets never exceeded the three percent mark (Fiure 5). Furthermore, until 7 all four markets were at both the ivin and receivin ends of net volatility transmissions, with almost equal manitudes. Thins chaned dramatically since January 8. Net volatility spillovers from the stock market stayed positive throuhout the several staes of the crisis, reachin as hih as six percent after the collapse of the Lehman Brothers in September 8. As we have already introduced the net spillover and net pairwise spillover plots, we can now have a detailed analysis of the spillovers from each market to the others usin Fiures 5 and 6. From 999 to 9, there were three major episodes of net volatility spillovers takin place from the stock market to other markets (Fiure 5a): durin, in thru 3, and after January 8. In our sample period, the first round of volatility spillovers took place from the stock market with the burst of the technoloy bubble in. As the troubles of the technoloy stocks intensified after March, the spillover index reached close to percent in the second throuh the last quarter of (Fiure ). At the time, the bulk of the volatility spillovers from the stock market were transmitted first to the bond, and then, to the commodity markets (Fiure 6a and 6b). 4
16 The second period when the stock market was a net transmitter of volatility to other markets spanned from the second half of to the third quarter of 3. Technoloy stocks continued to be under pressure until October as the Nasdaq Composite Index hit its lowest level since 997. In addition, the Iraqi crisis and the prospects of a war in the reion increased volatility in the US stock markets. 9 Durin this episode, total spillover index increased from 7.5 percent in June to 5 percent in June 3. Net volatility spillovers from the stock market reached close to 3 percent (Fiure 5a), and affected mostly the bond market (Fiure 6a). The fact that stock market was at the same time a net receiver of volatility from the commodity market (Fiure 6b) shows the link between increased volatility in stock markets and the impendin Iraqi War at the time. While the first two episodes of net volatility spillovers from the stock market were important, the third took place durin the worst financial crisis that hit the lobal financial markets. Since January 8, the total spillovers jumped to above 3 percent twice, in the first quarter and the fourth quarters of 8. Durin these two bouts of hefty volatility spillovers across financial markets, net spillovers from the stock market jumped to more than three and seven percents, respectively (Fiure 5a). The volatility from the stock market was transmitted to all three markets, but especially to the FX market (close to five percent) followin the collapse of the Lehman Brothers (Fiure 6c). Actually, durin the lobal financial crisis FX market also received sizeable net volatility spillovers from the bond market (Fiure 6e) as well as the commodity market (Fiure 6f). Net volatility spillovers from the bond market tend to be smaller than net spillovers from other markets. We identify three episodes of net volatility spillovers from the bond 9 Leih et al. (3) showed that a percentae point rise in the probability of a war on Iraq lowered the S&P5 by about one and a half percentae points. 5
17 market: the second half of throuh ; the end of 5 throuh 6; and throuhout 7 (Fiure 5b). In, the spillovers went in the direction of the FX market (Fiure 6e). In, on the other hand, the bulk of volatility spillovers from the bond market were transmitted to the stock market (Fiure 6a) and the commodity market (Fiure 6d). In the second half of 5 and the first half of 6 spillovers from the bond market were transmitted mostly to the FX market. In 7, on the other hand, the bond market spillovers affected the FX market mostly followed by the commodity market. We identify four episodes of net volatility spillovers from the commodity markets: Throuhout, in the first five months of 3 (before and immediately after the invasion of Iraq in March 3), in late 4 and throuh 5, and in the second half of 9 (Fiure 5c). Durin 3, the commodity market was a net transmitter of volatility (Fiure 5c). The oil prices started to increase from less than $ at the end of to close to $4 by February 3, before fallin to almost $5 by the end of April 3. Volatility spillovers from commodity markets increased in 3 just before and durin the invasion of Iraq by U.S. forces. Volatility spillovers from the commodity markets also increased, at the end of 4 and early 5, when the sure in Chinese demand for oil and metals surprised investors sendin commodity prices hiher (these shocks mostly transmitted to the bond and FX markets, See Fiures 6d and 6e), and especially from March 9 throuh September 9 (shocks mostly transmitted to the FX market). The volatility shocks in the commodity market in and durin the initial phases of the Iraqi invasion spilled over mostly to the stock market (Fiure 6b), but also to the bond market (Fiure 6d). Durin the late 4early 5 and the first half of 8, the volatility shocks in the commodity market mostly spilled over to the bond market (Fiure 6d), but also to the FX market (Fiure 6f). While 6
18 commodity market was a net recipient of modest levels of volatility shocks from the stock and bond markets, it was a net transmitter to the FX market durin 9. In the case of FX markets, there were three major episodes of positive net spillovers. Net volatility spillovers from FX markets had little impact on volatility in other markets, perhaps with the exception of the modest spillovers at the end of and early, from the end of throuh first half of 3, and finally in the second half of 6 (Fiure 5d). Net volatility spillovers from the FX market increased at the end of and early. It also increased in May 6, followin the FED s decision to tihten the monetary policy further (Fiure 5d). In both episodes, the volatility shocks in the FX market spilled over to the stock market and the commodity market (Fiures 6c and 6f). 4. Concludin Remarks We have provided both ross and net directional spillover measures that are independent of the orderin used for volatility forecast error variance decompositions. When applied to U.S. financial markets, our measures shed new liht on the nature of crossmarket volatility transmission, pinpointin the importance durin the recent crisis of volatility spillovers from the stock market to other markets. We are of course not the first to consider issues related to volatility spillovers (e.., Enle et al. 99; Kin et al., 994; Edwards and Susmel, ), but our approach is very different. It produces continuouslyvaryin indexes (unlike, for example, the hih state / low state indicator of Edwards and Susmel), and it is econometrically tractable even for very lare numbers of assets. Althouh it is beyond the scope of this paper, it will be interestin in 7
19 future work to understand better the relationship of our spillover measure to a variety of others based on measures ranin from traditional (albeit timevaryin) correlations (e.., Enle,, 9) to the recentlyintroduced CoVaR of Adrian and Brunnermeier (8). 8
20 References Adrian, T. and Brunnermeier, M. (8), CoVaR, Staff Report 348, Federal Reserve Bank of New York. Alizadeh, S., Brandt, M.W. and Diebold, F.X. (), RaneBased Estimation of Stochastic Volatility Models, Journal of Finance, 57, Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and Diebold, F.X. (6), Practical Volatility and Correlation Modelin for Financial Market Risk Manaement, in M. Carey and R. Stulz (eds.), Risks of Financial Institutions, University of Chicao Press for NBER, Diebold, F.X. and Yilmaz, K. (9), Measurin Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets, Economic Journal, 9, Enle, R.F. (), Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models, Journal of Business and Economic Statistics,, Enle, R.F. (9), Anticipatin Correlations. Princeton: Princeton University Press. Enle, R.F., Ito, T. and Lin, W.L. (99), Meteor Showers or Heat Waves: Heteroskedastic IntraDaily Volatility in the Forein Exchane Market, Econometrica, 58, Forbes, K.J. and Riobon, R. (), No Contaion, Only Interdependence: Measurin Stock Market Comovements, Journal of Finance, 57, 36. Kin, M., Sentana, E. and Wadhwani, S. (994), Volatility and Links Between National Stock Markets, Econometrica, 6, Koop, G., Pesaran, M.H., and Potter, S.M. (996), Impulse Response Analysis in Non Linear Multivariate Models, Journal of Econometrics, 74, Leih, A., Wolfers, J., and Zitzewitz, E. (3), What Do Financial Markets Think of War in Iraq?, NBER Workin Paper #9587, March. Parkinson, M. (98), The Extreme Value Method for Estimatin the Variance of the Rate of Return, Journal of Business, 53,
VOLATILITY SPILLOVERS AMONG ALTERNATIVE ENERGY, OIL AND TECHNOLOGY GLOBAL MARKETS. Elena Renedo Sánchez
VOLATILITY SPILLOVERS AMONG ALTERNATIVE ENERGY, OIL AND TECHNOLOGY GLOBAL MARKETS Elena Renedo Sánchez Trabajo de investigación 015/015 Master en Banca y Finanzas Cuantitativas Tutora: Dra. Pilar Soriano
More informationMEASURING FINANCIAL ASSET RETURN AND VOLATILITY SPILLOVERS, WITH APPLICATION TO GLOBAL EQUITY MARKETS*
The Economic Journal, 119 (January), 158 171.. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. MEASURING FINANCIAL ASSET RETURN AND
More informationSpillover effects among gold, stocks, and bonds
106 JCC Journal of CENTRUM Cathedra by Steven W. Sumner Ph.D. Economics, University of California, San Diego, USA Professor, University of San Diego, USA Robert Johnson Ph.D. Economics, University of Oregon,
More informationAEROSOL STATISTICS LOGNORMAL DISTRIBUTIONS AND dn/dlogd p
Concentration (particle/cm3) [e5] Concentration (particle/cm3) [e5] AEROSOL STATISTICS LOGNORMAL DISTRIBUTIONS AND dn/dlod p APPLICATION NOTE PR001 Lonormal Distributions Standard statistics based on
More informationCorrelation of International Stock Markets Before and During the Subprime Crisis
173 Correlation of International Stock Markets Before and During the Subprime Crisis Ioana Moldovan 1 Claudia Medrega 2 The recent financial crisis has spread to markets worldwide. The correlation of evolutions
More informationXBRL Generation, as easy as a database. Christelle BERNHARD  christelle.bernhard@addactis.com Consultant & Deputy Product Manager of ADDACTIS Pillar3
XBRL Generation, as easy as a database. Christelle BERNHARD  christelle.bernhard@addactis.com Consultant & Deputy Product Manaer of ADDACTIS Pillar3 Copyriht 2015 ADDACTIS Worldwide. All Rihts Reserved
More informationExamining the Relationship between ETFS and Their Underlying Assets in Indian Capital Market
2012 2nd International Conference on Computer and Software Modeling (ICCSM 2012) IPCSIT vol. 54 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V54.20 Examining the Relationship between
More informationOn the Influence of the Prediction Horizon in Dynamic Matrix Control
International Journal of Control Science and Enineerin 203, 3(): 2230 DOI: 0.5923/j.control.203030.03 On the Influence of the Prediction Horizon in Dynamic Matrix Control Jose Manue l Lope zgue de,*,
More informationTime varying volatility indexes and their determinants: Evidence from developed and emerging stock markets
Time varying volatility indexes and their determinants: Evidence from developed and emerging stock markets Nalin Prasad a, Andrew Grant b and SukJoong Kim c Discipline of Finance The University of Sydney
More informationContemporaneous Spillover among Equity, Gold, and Exchange Rate Implied Volatility Indices
Contemporaneous Spillover among Equity, Gold, and Exchange Rate Implied Volatility Indices Ihsan Ullah Badshah, Bart Frijns*, Alireza TouraniRad Department of Finance, Faculty of Business and Law, Auckland
More informationThe LongRun Performance of the New Zealand Stock Markets: 18992012
The LongRun Performance of the New Zealand Stock Markets: 18992012 Bart Frijns * & Alireza TouraniRad Auckland Centre for Financial Research (ACFR) Department of Finance, Faculty of Business and Law,
More informationModelling Intraday Volatility in European Bond Market
Modelling Intraday Volatility in European Bond Market Hanyu Zhang ICMA Centre, Henley Business School Young Finance Scholars Conference 8th May,2014 Outline 1 Introduction and Literature Review 2 Data
More informationVolatility Spillovers across South African Asset Classes during Domestic and Foreign Financial Crises
Volatility Spillovers across South African Asset Classes during Domestic and Foreign Financial Crises Andrew S. Duncan and Alain Kabundi Working paper 202 February 2011 Volatility Spillovers across South
More informationStock Market Volatility and the Business Cycle
Burkhard Raunig, Johann Scharler 1 Refereed by: Johann Burgstaller, Johannes Kepler University Linz In this paper we provide a review of the literature on the link between stock market volatility and aggregate
More informationImpacts of Manufacturing Sector on Economic Growth in Ethiopia: A Kaldorian Approach
Available online at http://www.journaldynamics.or/jbems Journal of Business Economics and Manaement Sciences Vol. 1(1), pp.18, December 2014 Article ID: JBEMS14/011 2014 Journal Dynamics Oriinal Research
More information9 Hedging the Risk of an Energy Futures Portfolio UNCORRECTED PROOFS. Carol Alexander 9.1 MAPPING PORTFOLIOS TO CONSTANT MATURITY FUTURES 12 T 1)
Helyette Geman c0.tex V  0//0 :00 P.M. Page Hedging the Risk of an Energy Futures Portfolio Carol Alexander This chapter considers a hedging problem for a trader in futures on crude oil, heating oil and
More informationInterest rate volatility in historical perspective
Interest rate volatility in historical perspective Harvey Rosenblum and Steven Strongin On October 6, 1979, the Federal Reserve changed its procedures for implementing monetary policy. Prior to that date,
More informationHewlettPackard 12C Tutorial
To bein, look at the ace o the calculator. Every key (except the arithmetic unction keys in the ar riht column and the ive keys on the bottom let row) has two or three unctions: each key s primary unction
More informationFinancial Markets of Emerging Economies Part I: Do Foreign Investors Contribute to their Volatility? Part II: Is there Contagion from Mature Markets?
1 Financial Markets of Emerging Economies Part I: Do Foreign Investors Contribute to their Volatility? Part II: Is there Contagion from Mature Markets? Martin T. Bohl Westfälische WilhelmsUniversity Münster,
More informationStock market integration: A multivariate GARCH analysis on Poland and Hungary. Hong Li* School of Economics, Kingston University, Surrey KT1 2EE, UK
Stock market integration: A multivariate GARCH analysis on Poland and Hungary Hong Li* School of Economics, Kingston University, Surrey KT1 2EE, UK Ewa Majerowska Department of Econometrics, University
More informationHPC Scheduling & Job Prioritization WHITEPAPER
HPC Schedulin & Job Prioritization WHITEPAPER HPC Schedulin & Job Prioritization Table of contents 3 Introduction 3 The Schedulin Cycle 5 Monitorin The Schedulin Cycle 6 Conclusion 2 HPC Schedulin & Job
More informationImplied volatility transmissions between Thai and selected advanced stock markets
MPRA Munich Personal RePEc Archive Implied volatility transmissions between Thai and selected advanced stock markets Supachok Thakolsri and Yuthana Sethapramote and Komain Jiranyakul Public Enterprise
More informationHigh Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution and Patterns in Returns
High Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution and Patterns in Returns David Bowen a Centre for Investment Research, UCC Mark C. Hutchinson b Department of Accounting, Finance
More informationMEASURING RETURN AND VOLATILITY SPILLOVERS IN GLOBAL FINANCIAL MARKETS
MEASURING RETURN AND VOLATILITY SPILLOVERS IN GLOBAL FINANCIAL MARKETS PONGSAKORN SUWANPONG 1 FACULTY OF ECONOMICS, CHULALONGKORN UNIVERSITY BANGKOK, THAILAND Abstract This paper purposely measures return
More informationStock market booms and real economic activity: Is this time different?
International Review of Economics and Finance 9 (2000) 387 415 Stock market booms and real economic activity: Is this time different? Mathias Binswanger* Institute for Economics and the Environment, University
More informationInternet Appendix to Stock Market Liquidity and the Business Cycle
Internet Appendix to Stock Market Liquidity and the Business Cycle Randi Næs, Johannes A. Skjeltorp and Bernt Arne Ødegaard This Internet appendix contains additional material to the paper Stock Market
More informationDEMB Working Paper Series N. 53. What Drives US Inflation and Unemployment in the Long Run? Antonio Ribba* May 2015
DEMB Working Paper Series N. 53 What Drives US Inflation and Unemployment in the Long Run? Antonio Ribba* May 2015 *University of Modena and Reggio Emilia RECent (Center for Economic Research) Address:
More informationComovement of State s Mortgage Default Rates: A Dynamic Factor Analysis Approach
Comovement of State s Mortgage Default Rates: A Dynamic Factor Analysis Approach Yaw OwusuAnsah 1 Latest Edition: June 10 th, 2012 Abstract Underestimated default correlations of the underlying assets
More informationWater Quality and Environmental Treatment Facilities
Geum Soo Kim, Youn Jae Chan, David S. Kelleher1 Paper presented April 2009 and at the Teachin APPAMKDI Methods International (Seoul, June Seminar 1113, on 2009) Environmental Policy direct, twostae
More informationCapital Constraints, Lending over the Cycle and the Precautionary Motive: A Quantitative Exploration (Working Paper)
Capital Constraints, Lending over the Cycle and the Precautionary Motive: A Quantitative Exploration (Working Paper) Angus Armstrong and Monique Ebell National Institute of Economic and Social Research
More informationEmployment, Wage Structure, and the Economic Cycle: Differences between Immigrants and Natives in Germany and the UK
Employment, Wae Structure, and the Economic Cycle: Differences between Immirants and Natives in Germany and the UK Christian Dustmann (UCL, CReAM), Albrecht Glitz (Universitat Pompeu Fabra, CReAM) and
More information44 ECB STOCK MARKET DEVELOPMENTS IN THE LIGHT OF THE CURRENT LOWYIELD ENVIRONMENT
Box STOCK MARKET DEVELOPMENTS IN THE LIGHT OF THE CURRENT LOWYIELD ENVIRONMENT Stock market developments are important for the formulation of monetary policy for several reasons. First, changes in stock
More informationBlack Box Trend Following Lifting the Veil
AlphaQuest CTA Research Series #1 The goal of this research series is to demystify specific black box CTA trend following strategies and to analyze their characteristics both as a standalone product as
More informationCurrent account deficit 10. Private sector Other public* Official reserve assets
Australian Capital Flows and the financial Crisis Introduction For many years, Australia s high level of investment relative to savings has been supported by net foreign capital inflow. This net capital
More informationChapter 1. Vector autoregressions. 1.1 VARs and the identi cation problem
Chapter Vector autoregressions We begin by taking a look at the data of macroeconomics. A way to summarize the dynamics of macroeconomic data is to make use of vector autoregressions. VAR models have become
More informationChanges in the Relationship between Currencies and Commodities
Bank of Japan Review 2012E2 Changes in the Relationship between Currencies and Commodities Financial Markets Department Haruko Kato March 2012 This paper examines the relationship between socalled commodity
More informationBank of Japan Review. Global correlation among government bond markets and Japanese banks' market risk. February 2012. Introduction 2012E1
Bank of Japan Review 212E1 Global correlation among government bond markets and Japanese banks' market risk Financial System and Bank Examination Department Yoshiyuki Fukuda, Kei Imakubo, Shinichi Nishioka
More informationNon Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization
Non Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization Jean Damien Villiers ESSEC Business School Master of Sciences in Management Grande Ecole September 2013 1 Non Linear
More informationThe Investment Performance of Rare U.S. Coins By: Raymond E. Lombra, Ph.D.
The Investment Performance of Rare U.S. Coins By: Raymond E. Lombra, Ph.D. An independent study of the investment performance of rare U.S. coins for the period January 1979 to December 2011. Analysis by
More informationContemporaneous spillover effects between the US and the UK
Contemporaneous spillover effects between the US and the UK Marinela Adriana Finta Bart Frijns Alireza TouraniRad Auckland University of Technology Auckland, New Zealand Corresponding Author: Marinela
More informationSAFE HAVEN ASSETS: THE GOLD PARADOX Written by: Kwazi Mbhele Junior Research Analyst at Glacier by Sanlam
FUNDS ON FRIDAY b y G l a c i e r R e s e a r c h 29 A p r i l 2 0 1 6 V o l u m e 8 5 9 SAFE HAVEN ASSETS: THE GOLD PARADOX Written by: Kwazi Mbhele Junior Research Analyst at Glacier by Sanlam Financial
More informationNo Contagion, Only Interdependence: Measuring Stock Market Comovements
THE JOURNAL OF FINANCE VOL. LVII, NO. 5 OCTOBER 2002 No Contagion, Only Interdependence: Measuring Stock Market Comovements KRISTIN J. FORBES and ROBERTO RIGOBON* ABSTRACT Heteroskedasticity biases tests
More informationWhat Drives International Equity Correlations? Volatility or Market Direction? *
Working Paper 941 Departamento de Economía Economic Series (22) Universidad Carlos III de Madrid June 29 Calle Madrid, 126 2893 Getafe (Spain) Fax (34) 916249875 What Drives International Equity Correlations?
More informationReint Gropp. How Important Are Hedge Funds in a Crisis? Policy Letter No. 23
Reint Gropp How Important Are Hedge Funds in a Crisis? Policy Letter No. 23 This article is reprinted from the Federal Reserve Bank of San Francisco Economic Letter of April 14, 2014. The opinions expressed
More informationThe Microstructure of Currency Markets: an Empirical Model of Intraday Return and BidAsk Spread Behavior
The Microstructure of Currency Marets: an Empirical Model of Intraday Return and BidAs Spread Behavior Paolo Pasquariello Stern School of Business New Yor University Revision July 9 th 00 Abstract This
More informationBand Gap Energy in Silicon
Band Gap Enery in Silicon Jeremy J. Low, Michael L. Kreider, Drew P. Pulsifer Andrew S. Jones and Tariq H. Gilani Department of Physics Millersville University P. O. Box 12 Millersville, Pennsylvania 17551
More informationTHE SIMPLE PENDULUM. Objective: To investigate the relationship between the length of a simple pendulum and the period of its motion.
THE SIMPLE PENDULUM Objective: To investiate the relationship between the lenth of a simple pendulum and the period of its motion. Apparatus: Strin, pendulum bob, meter stick, computer with ULI interface,
More informationVolatility spillovers among the Gulf Arab emerging markets
University of Wollongong Research Online University of Wollongong in Dubai  Papers University of Wollongong in Dubai 2010 Volatility spillovers among the Gulf Arab emerging markets Ramzi Nekhili University
More informationChapter Seven STOCK SELECTION
Chapter Seven STOCK SELECTION 1. Introduction The purpose of Part Two is to examine the patterns of each of the main Dow Jones sectors and establish relationships between the relative strength line of
More informationICI RESEARCH PERSPECTIVE
ICI RESEARCH PERSPECTIVE 1401 H STREET, NW, SUITE 1200 WASHINGTON, DC 20005 2023265800 WWW.ICI.ORG DECEMBER 2013 VOL. 19, NO. 12 WHAT S INSIDE 2 Introduction 3 EBRI/ICI 401(k) Database 8 YearEnd 2012
More informationPrice and Volatility Transmission in International Wheat Futures Markets
ANNALS OF ECONOMICS AND FINANCE 4, 37 50 (2003) Price and Volatility Transmission in International Wheat Futures Markets Jian Yang * Department of Accounting, Finance and Information Systems Prairie View
More informationWhat Drives Natural Gas Prices?
A Structural VAR Approach  Changing World of Natural Gas I Moscow I 27th September I Sebastian Nick I Stefan Thoenes Institute of Energy Economics at the University of Cologne Agenda 1.Research Questions
More informationSolutions to Homework Set #3 Phys2414 Fall 2005
Solution Set #3 1 Solutions to Homework Set #3 Phys2414 Fall 2005 Note: The numbers in the boxes correspond to those that are enerated by WebAssin. The numbers on your individual assinment will vary. Any
More informationTotal Portfolio Performance Attribution Methodology
Total ortfolio erformance Attribution Methodoloy Morninstar Methodoloy aper May 31, 2013 2013 Morninstar, Inc. All rihts reserved. The information in this document is the property of Morninstar, Inc. Reproduction
More informationfi360 Asset Allocation Optimizer: RiskReturn Estimates*
fi360 Asset Allocation Optimizer: RiskReturn Estimates* Prepared for fi360 by: Richard Michaud, Robert Michaud, Daniel Balter New Frontier Advisors LLC Boston, MA 02110 February 2015 * 2015 New Frontier
More informationChapter 4: Vector Autoregressive Models
Chapter 4: Vector Autoregressive Models 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie IV.1 Vector Autoregressive Models (VAR)...
More informationA Volatility Spillover among Sector Index of International Stock Markets
Journal of Money, Investment and Banking ISSN 1450288X Issue 22 (2011) EuroJournals Publishing, Inc. 2011 http://www.eurojournals.com/jmib.htm A Volatility Spillover among Sector Index of International
More informationThe Impact of Oil Price Shocks on U.S. Bond Market Returns
Crawford School of Public Policy CAMA Centre for Applied Macroeconomic Analysis The Impact of Oil Price Shocks on U.S. Bond Market Returns CAMA Working Paper 33/2014 April 2014 Wensheng Kang Department
More informationShould we Really Care about Building Business. Cycle Coincident Indexes!
Should we Really Care about Building Business Cycle Coincident Indexes! Alain Hecq University of Maastricht The Netherlands August 2, 2004 Abstract Quite often, the goal of the game when developing new
More informationThank you very much. I am honored to follow one of my heroes
Earnings, Inflation, and Future Stock and Bond Returns 1 JEREMY J. SIEGEL Russell E. Palmer Professor of Finance The Wharton School University of Pennsylvania Thank you very much. I am honored to follow
More informationA Privacy Mechanism for Mobilebased Urban Traffic Monitoring
A Privacy Mechanism for Mobilebased Urban Traffic Monitorin Chi Wan, Hua Liu, Bhaskar Krishnamachari, Murali Annavaram Tsinhua University, Beijin, China sonicive@mail.com University of Southern California,
More informationImpact of Oil Prices on the Indian Economy
RESEARCH NOTE A. Aparna Abstract Crude oil prices play a very significant role on the economy of any country. India's growth story hovers around the import of oil as India imports 70% of its crude requirements.
More informationBoard of Governors of the Federal Reserve System. International Finance Discussion Papers. Number 863. June 2006
Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 863 June 2006 Transmission of Volatility and Trading Activity in the Global Interdealer Foreign Exchange
More informationElucidating the Relationship among Volatility Index, US Dollar Index and Oil Price
2324 July 25, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 97892269795 Elucidating the Relationship among Volatility Index, US Dollar Index and Oil Price John WeiShan Hu* and HsinYi Chang**
More informationExtending Factor Models of Equity Risk to Credit Risk and Default Correlation. Dan dibartolomeo Northfield Information Services September 2010
Extending Factor Models of Equity Risk to Credit Risk and Default Correlation Dan dibartolomeo Northfield Information Services September 2010 Goals for this Presentation Illustrate how equity factor risk
More informationChanging Pattern of Meat Consumption in Australia
May 213 Chanin Pattern of Meat Consumption in Australia by Lucille Won, E A Selvanathan and Saroja Selvanathan Griffith Business School Griffith University Nathan, Queensland 4111 AUSTRALIA Abstract: The
More informationAsymmetric behavior of volatility in gasoline prices across different regions of the United States
Asymmetric behavior of volatility in gasoline prices across different regions of the United States ABSTRACT S. Aun Hassan Colorado State University Pueblo Hailu Regassa Colorado State University Pueblo
More informationThe Monetary Stabilization Bond Spread as a Signal for Financial Crisis
The Monetary Stabilization Bond Spread as a Signal for Financial Crisis Jinyong Kim KAIST YongCheol Kim University of WisconsinMilwaukee Abstract This paper explores the potential signaling role of the
More informationPriceEarnings Ratios: Growth and Discount Rates
PriceEarnings Ratios: Growth and Discount Rates Andrew Ang Columbia University and NBER Xiaoyan Zhang Purdue University This Version: 20 May 2011 We thank Geert Bekaert, Sigbjørn Berg, and Tørres Trovik
More informationStock Markets Correlation: before and during the Crisis Analysis *
Theoretical and Applied Economics Volume XVIII (2011), No. 8(561), pp. 111122 Stock Markets Correlation: before and during the Crisis Analysis * Ioana MOLDOVAN Bucharest Academy of Economic Studies ioana.moldovan@economie.ase.ro
More informationRisk and Return in the Canadian Bond Market
Risk and Return in the Canadian Bond Market Beyond yield and duration. Ronald N. Kahn and Deepak Gulrajani (Reprinted with permission from The Journal of Portfolio Management ) RONALD N. KAHN is Director
More informationAustralian Implied Volatility Index
Key words: stock market volatility; S&P/ASX 200 Index; ValueatRisk; options pricing models Australian Implied Volatility Index Volatility implied from option prices is widely regarded as the market s
More informationImpact of foreign portfolio investments on market comovements: Evidence from the emerging Indian stock market
Impact of foreign portfolio investments on market comovements: Evidence from the emerging Indian stock market Sunil Poshakwale and Chandra Thapa Cranfield School of Management, Cranfield University, Cranfield,
More informationthe UK Marinela Adriana Finta, Bart Frijns, Alireza TouraniRad, Department of Finance, Faculty of Business and Law,
Contemporaneous spillover effects between the US and the UK Marinela Adriana Finta, Bart Frijns, Alireza TouraniRad, Department of Finance, Faculty of Business and Law, Auckland University of Technology,
More informationInterest Rates and Inflation: How They Might Affect Managed Futures
Faced with the prospect of potential declines in both bonds and equities, an allocation to managed futures may serve as an appealing diversifier to traditional strategies. HIGHLIGHTS Managed Futures have
More informationProjectile Motion THEORY. r s = s r. t + 1 r. a t 2 (1)
Projectile Motion The purpose of this lab is to study the properties of projectile motion. From the motion of a steel ball projected horizontally, the initial velocity of the ball can be determined from
More informationVolatility: Implications for Value and Glamour Stocks
Volatility: Implications for Value and Glamour Stocks November 2011 Abstract 11988 El Camino Real Suite 500 P.O. Box 919048 San Diego, CA 921919048 858.755.0239 800.237.7119 Fax 858.755.0916 www.brandes.com/institute
More informationThe Composition of Metals and Alloys
1 The Composition of Metals and Alloys Metals are shiny, malleable substances that conduct heat and electricity. They comprise the larest class of elements in the Periodic Table. All metals except mercury
More informationOver the past several years, Americans have
Industry Securities Industry Bears, bulls, and brokers: employment trends in the securities industry in the securities industry strongly correlates with stock market value; however, market volume does
More informationTesting for Granger causality between stock prices and economic growth
MPRA Munich Personal RePEc Archive Testing for Granger causality between stock prices and economic growth Pasquale Foresti 2006 Online at http://mpra.ub.unimuenchen.de/2962/ MPRA Paper No. 2962, posted
More informationVariety Gains from Trade Integration in Europe
Variety Gains from Trade Interation in Europe d Artis Kancs a,b,c,, Damiaan Persyn a,b,d a IPTS, European Commission DG Joint Research Centre, E41092 Seville, Spain b LICOS, University of Leuven, B3000,
More informationModeling the Dynamics of Correlations Among International Equity Volatility Indices
Modeling the Dynamics of Correlations Among International Equity Volatility Indices Moloud Rahmaniani 1 Department of Accountancy and Finance Otago Business School, University of Otago Dunedin 9054, New
More informationRising Wage Inequality Within Firms: Evidence from Japanese health insurance society data
RIETI Discussion Paper Series 12E039 Risin Wae Inequality Within Firms: Evidence from Japanese health insurance society data SAITO Yukiko RIETI KOUNO Toshiaki Fujitsu Research Institute The Research
More informationDiscussion of Capital Injection, Monetary Policy, and Financial Accelerators
Discussion of Capital Injection, Monetary Policy, and Financial Accelerators Karl Walentin Sveriges Riksbank 1. Background This paper is part of the large literature that takes as its starting point the
More informationThe Influence of Crude Oil Price on Chinese Stock Market
The Influence of Crude Oil Price on Chinese Stock Market Xiao Yun, Department of Economics Pusan National University 2,Busandaehakro 63beongil, Geumjeonggu, Busan 609735 REPUBLIC OF KOREA a101506e@nate.com
More informationThe Macroeconomic Effects of Debt and EquityBased Capital Inflows *
Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper No. 214 http://www.dallasfed.org/assets/documents/institute/wpapers/214/214.pdf The Macroeconomic Effects of Debt
More informationTime Series Analysis of Wind Speed Using VAR and the Generalized Impulse Response Technique
Time Series Analysis of Wind Speed Using VAR and the Generalized Impulse Response Technique Bradley T. Ewing Rawls Professor of Operations Management Area of Information Systems & Quantitative Sciences
More informationThe Effect of Stock Prices on the Demand for Money Market Mutual Funds James P. Dow, Jr. California State University, Northridge Douglas W. Elmendorf Federal Reserve Board May 1998 We are grateful to Athanasios
More informationRISK ASSESSMENT OF LIFE INSURANCE CONTRACTS: A COMPARATIVE STUDY IN A LÉVY FRAMEWORK
RISK ASSESSMENT OF LIFE INSURANCE CONTRACTS: A COMARATIVE STUDY IN A LÉVY FRAMEWORK Nadine Gatzert University of St. Gallen, Institute of Insurance Economics, Kirchlistrasse, 9010 St. Gallen, Switzerland,
More informationKiyohiko G Nishimura: Financial factors in commodity markets
Kiyohiko G Nishimura: Financial factors in commodity markets Speech by Mr Kiyohiko G Nishimura, Deputy Governor of the Bank of Japan, at the Paris EUROPLACE International Financial Forum, Tokyo, 28 November
More informationInternet Connectivity for Mobile Ad Hoc Networks Using Hybrid Adaptive Mobile Agent Protocol
The International Arab Journal of Information Technoloy, Vol. 5, No. 1, January 2008 25 Internet Connectivity for Mobile Ad Hoc Networks Usin Hybrid Adaptive Mobile Aent Protocol Velmuruan Ayyadurai 1
More informationDiminished Expectations, Double Dips, and External Shocks: The Decade After the Fall
MPRA Munich Personal RePEc Archive Diminished Expectations, Double Dips, and External Shocks: The Decade After the Fall Carmen Reinhart and Vincent Reinhart University of Maryland, College Park, Department
More informationWorking Papers. Cointegration Based Trading Strategy For Soft Commodities Market. Piotr Arendarski Łukasz Postek. No. 2/2012 (68)
Working Papers No. 2/2012 (68) Piotr Arendarski Łukasz Postek Cointegration Based Trading Strategy For Soft Commodities Market Warsaw 2012 Cointegration Based Trading Strategy For Soft Commodities Market
More informationChapter 6. Modeling the Volatility of Futures Return in Rubber and Oil
Chapter 6 Modeling the Volatility of Futures Return in Rubber and Oil For this case study, we are forecasting the volatility of Futures return in rubber and oil from different futures market using Bivariate
More informationVI. Real Business Cycles Models
VI. Real Business Cycles Models Introduction Business cycle research studies the causes and consequences of the recurrent expansions and contractions in aggregate economic activity that occur in most industrialized
More informationDoes the Spot Curve Contain Information on Future Monetary Policy in Colombia? Por : Juan Manuel Julio Román. No. 463
Does the Spot Curve Contain Information on Future Monetary Policy in Colombia? Por : Juan Manuel Julio Román No. 463 2007 tá  Colombia  Bogotá  Colombia  Bogotá  Colombia  Bogotá  Colombia  Bogotá
More informationGLOBAL STOCK MARKET INTEGRATION  A STUDY OF SELECT WORLD MAJOR STOCK MARKETS
GLOBAL STOCK MARKET INTEGRATION  A STUDY OF SELECT WORLD MAJOR STOCK MARKETS P. Srikanth, M.Com., M.Phil., ICWA., PGDT.,PGDIBO.,NCMP., (Ph.D.) Assistant Professor, Commerce Post Graduate College, Constituent
More informationVOLATILITY FORECASTING FOR MUTUAL FUND PORTFOLIOS. Samuel Kyle Jones 1 Stephen F. Austin State University, USA Email: sjones@sfasu.
VOLATILITY FORECASTING FOR MUTUAL FUND PORTFOLIOS 1 Stephen F. Austin State University, USA Email: sjones@sfasu.edu ABSTRACT The return volatility of portfolios of mutual funds having similar investment
More informationComovements of the Korean, Chinese, Japanese and US Stock Markets.
World Review of Business Research Vol. 3. No. 4. November 2013 Issue. Pp. 146 156 Comovements of the Korean, Chinese, Japanese and US Stock Markets. 1. Introduction SungKy Min * The paper examines Comovements
More informationINTEREST RATES & REIT PERFORMANCE SEARCHING FOR A CORRELATION. APRIL 2014
PART ONE: INTEREST RATES & REIT PERFORMANCE SEARCHING FOR A CORRELATION. APRIL 2014 Burland East, CFA CEO, Portfolio Manager AACA* Creede Murphy Investment Analyst AACA* How do REIT share prices perform
More information