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A new variant of estimation approach to asymmetric stochastic volatilitymodel

1 Quantitative Research, JPMorgan Chase & Co., 277 Park Avenue, New York
2 Department of Statistics and Actuarial Science, University of Waterloo, 200 University AvenueWest, Waterloo, Ontario, Canada
3 School of Accounting and Finance, University of Waterloo, 200 University Avenue West, Waterloo,Ontario, Canada

Special Issue: Volatility of Prices of Financial Assets

This paper proposes a novel simulation-based inference for an asymmetric stochastic volatility model. An acceptance-rejection Metropolis-Hastings algorithm is developed for the simulation of latent states of the model. A simple and e cient algorithm is also developed for estimation of a heavy-tailed stochastic volatility model. Simulation studies show that our proposed methods give rise to reasonable parameter estimates. Our proposed estimation methods are then used to analyze a benchmark data set of asset returns.
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1.Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econometrics 31: 307–327.    

2.Bauwens L, Lubrano M (1998) Bayesian inference on GARCH models using the Gibbs sampler. Economet J 1: C23–C26.    

3.Broto C, Ruiz E (2004) Estimation methods for stochastic volatility models: a survey. J Econ Surv 18: 613–649.    

4.Carnero A, Pena D, Ruiz E (2003) Persistence and kurtosis in GARCH and stochastic volatility models J Financ Economet 2: 319–342.

5.Chib S, Greenberg E (1995) Understanding the Metropolis-Hastings Algorithm. American Statistician 49: 327–335.

6.Chib S, Nardarib F, Shephard N (2006) Analysis of high dimensional multivariate stochastic volatility models. J Econometrics 134: 341–371.    

7.Dobigeon N, Tourneret J (2010) Bayesian orthogonal component analysis for sparse representation. IEEE T Signal Proces 58: 2675–2685.    

8.Diebold FX, Guther TA, Tay AS (1998) Evaluating density forecasts with applications to financial risk management. Int Econ Rev 39: 863–883.    

9.Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50: 987–1007.    

10.Eraker B, Johannes M, Polson N (2003) The Impact ofJumps inVolatility and Returns. J Financ 58: 1269–1300.    

11.Geweke J (1993) Bayesian treatment of the independent Student-t linear model. J Appl Econom 8: S19–S40.    

12.Harvey AC, Shephard N (1996) Estimation of an asymmetric stochastic volatility model for asset returns. J Bus Econ Stat 14: 42-434.

13.Jacquier E, Polson NG, Rossi PE (2004) Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. J Econometrics 122: 185–212.    

14.Kawakatsu H (2007) Numerical integration-based Gaussian mixture filters for maximum likelihood estimation of asymmetric stochastic volatility models. Economet 10: 342–358.    

15.Kim S, Shephard N, Chib S (1998) Stochastic volatility: Likelihood inference and comparison with ARCH models. Rev Econ Stud 65: 361–393.    

16.Liesenfeld R, Richard J (2003) Univariate and multivariate stochastic volatility models: estimation and diagnostics. J Empiri Financ 45: 505–531.

17.Melino A, Turnbull SM (1990) Pricing foreign currencyoptions with stochastic volatility. J Econometrics 45: 239–265.    

18.Men Z (2012) Bayesian Inference for Stochastic Volatility Models. Ph.D. thesis, Department of Statistics and Actuarial Science at the University of Waterloo.

19.Men Z, McLeish D, Kolkiewicz A, et al. (2017) Comparison of Asymmetric Stochastic Volatility Models under Di erent Correlation Structures. J Appl Stat 44: 1350–1368.    

20.Men Z, Kolkiewicz A, Wirjanto TS (2015) Bayesian Analysis of Asymmetric Stochastic Conditional Duration Model. J Forecasting 34: 36–56.    

21.Mira A, Tierney L (2002) E ciency and Convergence Properties of Slice Samplers. Scand J Stat 29: 1–12.    

22.Neal RN (2003) Slice sampling. Annals Stat 31: 705–767.    

23.Omori Y, Chib S, Shephard N, et al. (2007) Stochastic volatility with leverage: Fast and e cient likelihood inference. J Econometrics 140: 425–449.    

24.Pitt MK, Shephard N (1999a) Time varying covariances: A factor stochastic volatility approach. Bayesian Stat 6: 547–570.

25.Pitt M, Shephard N (1999b) Filtering via simulation: Auxiliary particle filters. J Am Stat Assoc 94: 590–599.

26.Roberts GO, Rosenthal JS (1999) Convergence of Slice Sampler Markov Chains. J R Stat Soc B 61: 643–660.    

27.Shephard N, Pitt MK (1997) Likelihood Analysis of non-Gaussian Measurement Time Series. Biometrika 84: 653–667.    

28.Taylor SJ (1986) Modelling Financial Time Series, Chichester: Wiley.

29.Wirjanto TS, Kolkiewicz A, Men Z (2016) Bayesian Analysis of a Threshold Stochastic Volatility Model. J Forecasting 35: 462–476.    

30.Yu J (2005) On leverage in a stochastic volatility model. J Econometrics 127: 165–178.    

31.Yu J, Meyer R (2006) Multivariate stochastic volatility models: Bayesian estimation and model comparison. Economet Rev 51: 2218–2231.

32.Zhang X, King L (2008) Box-Cox stochastic volatility models with heavy-tails and correlated errors. J Empiri Financ 15: 549–566.    

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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