E. Maasoumi aiul M. Mc.\leer the s|)ecilication, e stimation, and ... Christian (iourieroux's paper, entitled "Continuous Time Wishart. PteKess fe)r Stochastic Risk," ...
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i»dels as well as simuladon smoothing, where the latent ve>Iatilities are sampled at once. Sampling the vector e)f latent ve)Iatilitie-s in one block can imprene the convergence of algorithms. Based e)n this KIS simulation sm(K)ther, the atithors demonstrate how a Baytsi.iii MCMC posterior anahsis of the parameters e)f alternative univariate SV and M.S\' models can be performed. Seve-ral lu-vv models are developed by Jun Yu and Renate Me-ve-r in \Multivariate Ste)chastic Volatility Me)elels: Bayesian Esdmation and Model Comparison." The papet shows that fully likelihood-based estimation and (e)mpari.sons of .MSV models ean be easily performed via a freelv available Bayesian software called WinBUGS. More-over, the authors introduce- te> the literatute several nt-vv spe-cilieations that are natural e-xtensie>tis of some existing models, one of which allows for time-varying correlatiein ce)e'fTicienLs. Ideas reladng to model speeilieadon aiul estimation are illustrated by fitdng nine M.S\' models te) a bivariate time series d.ua of weekly exchange rates, ine luding the s|)ecifieatioiis with Granger eatisalitv in volatility, time-vai-ying cortelatious. he-avvtailed error distribution.s, additive factor structure, and muUipIicative fae tor structure. The etnpirical results suggest that the me)st adequate spe-e ificadons are those- that allow for time-varving ce)rrelatie)n coefficients. Borusjungbacker and Siem Jan Kexipman present a novel estimation algorithm in "Monte- Carlo Likelihe)od Estimation for Three Multivariate Stochiistic Voladlitv Me)dels." The authors ne)te that estimating |)arameters in an .S\' model is a e hallenging task. Among other estimation methods and
.Miiltivtirifitc .Stiirhn.stir \'i)laliltt\
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appre)aches, efficient simuladon methods based on imporiaiue sampling have been developed for the Monte Carlo maximum likelihood estimation of univariate SV tnodels. The authors slu)w that itnpottanee sampling methods can be used in a general MS\' .setdng. Tlie sampling methods are comptJtadonally efficient. In eirder to illustrate tlu- versatility of this approach, three dillerent MSV models are esdmated for a standard data set. The empirical results are eeimpated with those from previous studit-s in the literature. Monte Carlo simulatie)n experiments based e)n parameter estimates from the standard data set are tised to show the effectiveness of the importance sampling methods. .\n interesdng ce)mbination of ste)ehastic and realized voladlity is examined in "A Range-Based Multivariate Stochastic X'olaiilitv Model for Exchange Rates" bv Ben Tims and Ronald Mahieu. Ihe authors present a parsime)nious multivariate mejdel lor exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates, in which the range is use-d as an approximadon for volatilitv. The MS\' model decomposes die logarithmic i"ange of each exchange rate into twe) indejiendent latf-ut factors, which are interpreted as the underlying e urrency-specific ce)mponents. Owing to the normality of the logarithmic volatility nu-asure. the model can be estimated conveniendy with standatd Kalman filter techniques. The results show that die model lits the exchange- rate data quite well. In pardcular, exchange tate news se-e-ins te) be ve-n currencv specific and allows identification of which eurrency contributes tnost to both exchange rate levels and the exchange rate. Michael Smith and Andrew Pitts develop a ne-vv nu)del in "Foreign Kxe hange Intervendon by tJie Bank e)f Japan: Bayesian Analvsis Using a Bivariate Stoehastic \'olatility Model." The authors use a bivariate .S\' model te) measuie the effect of intei-vention bv the- Bank of Japan (UOJ) on daily returns and volume in tlu- L SDA'en foreign exchange market. Missing observ^atieins are- accounted for, and a data-base-d Wishart prior lor the precision matrix of the transidon equadon errors is suggested. Empirical results suggest there is strong conditional heteroskerd.istie itv in the meancoriee ted volume, as well as contemporaneous correlation in dieobsei-vation and transition equadon errors. A dire shold model is used for the- BOJ reaction function, which is esdmated jointlv with the bivariate .S\' model via MCMC to account for endogeneitv between market volatility and the BOJ reaction funcde)n. The bivariate SV model is shown to produce estimates that more accuratelv capture spikes in sampling volatility than de>es a bivariate EGARCH model. Two novel MSV models are developed in \'VsynuTieuic NfultivariateStochastic Voladlitv" by Manabu Asai and Michael Mc.Meer. The authors pre)p()se- and aualvse two types of asymmetric MS\ models, namelv SV with ieverage (SV-L), which is based e)n the negative correladon betwe-e-n tbe intie)vations in the returns and volatilit). and .S\ with leverage and si/e
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effect (SV-t^E), which is based on the signs and magnittide of the returns. The paper derives the state space form for the logarithm of the scjuared le turns, which follow the muldvariate S\'-L model, and develops Monte C.ulo likelihood (MCL) estimation methe)ds feir the muldvariate- .S\'-L and SV-LSE models. The empirical results she)w that the multivariate S\'-Ii>E model fits the bivariate- and ttivariate rettirns of thte-e- well known data sets with respect to AIC and BIC me)re accurately than does the the multivai iate S\-L model, and that the utiivariate models should be rejected in favor of their bivaiiate and trivariate counterparts. We hope this eollection of contribudons by the leading experts in thefield will serve both the professionals and readers U-ss familiar with this vibrant area of research and financial analysis. It is e)ur ple-asute to thank all the contributors for sharing their innovadons and superb tee hnical knowhow in a timely manner and for participadng in the rigorous review process very tesponsively. ACKNOWLEDGMENT
The authors wish to thank Manabu Asai. Jid (iao. Siem Jan Koopman, Essie Maase)umi. Michael McAleer, Marcelo Medeireis, Alexander Philipen-, Neil Shephard, Michael Smitli. and Jun Yu for their helpful, insightful, and dmely reviews. The second author is most grateful for the financial suppeirt of the Anstnilian Research Council.
