GMM estimation of a realized stochastic volatility model: A Monte Carlo study

References

• Anatolyev, S. (2005). GMM, GEL, serial correlation and asymptotic bias. Econometrica 73:983–1002.
   Web of Science ®Google Scholar
• Andersen, T., Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Reviews 39:115–158.
   Web of Science ®Google Scholar
• Andersen, T., Bollerslev, T., Diebold, F., Labys, P. (2003). Modelling and forecasting realized volatility. Econometrica 71: 529–626.
   Google Scholar
• Andersen, T. SØrensen, B. (1996). GMM estimation of a stochastic volatility model: A monte carlo study. Journal of Business and Economic Statistics 14:329–352.
   Web of Science ®Google Scholar
• Andrews, D. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59:817–858.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O., Hansen, P., Lunde, A., Shephard, N. (2008). Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise. Econometrica 76:1481–1536.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O., Hansen, P., Lunde, A., Shephard, N. (2011). Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. Journal of Econometrics 162:149–169.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O., Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society B 64:253–280.
   Google Scholar
• Bollerslev, T., Zhou, H. (2006). A simple framework for gauging return-volatility regressions. Journal of Econometrics 131:123–150.
   Web of Science ®Google Scholar
• Carnero, A., Pena, D., Ruiz, E. (2004). Persistence and kurtosis in GARCH and stochastic volatility models. Journal of Financial Econometrics 2:319–342.
   Google Scholar
• Carrasco, M. (2012). A regularization approach to the many instruments problem. Journal of Econometrics 170(2):383–398.
   Web of Science ®Google Scholar
• Chaussé, P. (2010). Computing generalized method of moments and generalized empirical likelihood with r. Journal of Statistical Software 34(11):1–35.
   Web of Science ®Google Scholar
• Chaussé, P. (2011). Generalized Emprical Likelihood for a Continuum of Moment Conditions. University of Waterloo Working Paper.
   Google Scholar
• Corradi, V., Distaso, W. (2006). Semi-parametric comparison of stochastic volatility models using realized measures. Review of Economic Studies 73:635–667.
   Web of Science ®Google Scholar
• Danielsson, J., Richard, J. (1993). Accelerated gaussian importance sampler with application to dynamic latent variable models. Journal of Applied Econometrics 8:153–173.
   Web of Science ®Google Scholar
• Diebold, F., Strasser, G. (2013). On the correlation structure of microstructure noise: A financial economic approach. The Review of Economic Studies 80(4):1304–1337.
   Web of Science ®Google Scholar
• Dobrev, D., Szerszen, P. (2010). The information content of high-frequency data for estimating equity return models and forecasting risk. FEDS Working Paper No. 2010-45.
   Google Scholar
• Dominguez, M., Lobato, I. (2004). Consistent estimation of models defined by conditional moment restrictions. Econometrica 72:1601–1615.
   Web of Science ®Google Scholar
• Doran, H., Schmidt, P. (2006). GMM estimators with improved finite sample properties using principal components of the weighting matrix, wit an application to the dynamic panel data model. Journal of Econometrics 133:387–409.
   Web of Science ®Google Scholar
• Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation. Econometrica 50:987–1007.
   Web of Science ®Google Scholar
• Engle, R., Gallo, G. (2006). A multiple indicators model for volatility using intra-daily data. Journal of Econometrics 131:3–27.
   Web of Science ®Google Scholar
• Gallant, A., Tauchen, G. (1996). Which moments to match. Econometric Theory 12:657–681.
   Web of Science ®Google Scholar
• Ghysels, E., Harvey, A. C., Renault, E. (1996). Stochastic volatility. In: Maddala, G. S. Rao, C. R. eds. Statistical Methods in Finance. Amsterdam: North-Holland, pp. 119–191.
   Google Scholar
• Hall, A. (2000). Covariance matrix estimation and the power of the overidentifying restrictions test. Econometrica 68:1517–1527.
   Web of Science ®Google Scholar
• Hall, A., Inoue, A. (2003). The large sample behaviour of the GMM estimator in misspecified models. Journal of Econometrics 114:361–394.
   Web of Science ®Google Scholar
• Hansen, P., Huang, Z., Shek, H. (2012). RealizGARCH: A joint model for returns and realized measures of volatility. Journal of Applied Econometrics 27:877–906.
   Web of Science ®Google Scholar
• Hansen, P., Lunde, A. (2005). A realized variance for the whole day based on intermittent high-frequency data. Journal of Financial Econometrics 3:525–554.
   Google Scholar
• Harvey, A., Ruiz, E., Shephard, N. (1994). Multivariate stochastic variance models. Review of Economic Studies 61:247–264.
   Web of Science ®Google Scholar
• Harvey, A., Shephard, N. (1996). Estimation of an asymmetric stochastic volatility model for asset returns. Journal of Business and Economic Statistics 14:429–434.
   Web of Science ®Google Scholar
• Imhof, G. (1961). Computing the distribution of quadratic forms of normal variables. Biometrika 48:419–426.
   Web of Science ®Google Scholar
• Jacquier, E., Polson, N., Rossi, P. (2002). Bayesian analysis of stochastic volatility models (with discussion). Journal of Business and Economic Statistics 12:371–417.
   Google Scholar
• Knight, J., Yu, J. (2002). The empirical characteristic function in time series estimation. Econometric Theory 18:691–721.
   Web of Science ®Google Scholar
• Koopman, S., Scharth, M. (2013). The analysis of stochastic volatility in the presence of daily realised measures. Journal of Financial Econometrics 11:76–115.
   Web of Science ®Google Scholar
• Lee, H.-C., Halverson, D. (2004). Measuring performance of robust estimator for the variance of generalized gaussian distribution. Eighth International Symposium on Spread Spectrum Techniques and Applications. Sydney Australia. August 30–September 2.
   Google Scholar
• Noureldin, D., Shephard, N., Sheppard, K. (2012). Multivariate high frequency-based volatility (HEAVY) models. Journal of Applied Econometrics 27(6):907–933.
   Web of Science ®Google Scholar
• Park, B.-J. (2009). Risk-return relationship in equity markets: Using a robust gmm estimator for GARCH-M models. Quantitative Finance 9(1):93–104.
   Web of Science ®Google Scholar
• Ronchetti, E., Trojani, F. (2001). Robust inference with GMM estimators. Journal of Econometrics 101:37–69.
   Web of Science ®Google Scholar
• Shephard, N., Sheppard, K. (2010). Realising the future: Forecasting with high-frequency-based volatility (HEAVY) models. Journal of Applied Econometrics 25:197–231.
   Web of Science ®Google Scholar
• Takahashi, M., Omori, Y., Watanabe, T. (2009). Estimating stochastic volatility models using daily returns and realized volatility simultaneously. Computational Statistics and Data Analysis 53:2404–2426.
   Web of Science ®Google Scholar
• Taylor, S. (1986). Modelling Financial Time Series. Chichester UK: Wiley.
   Google Scholar
• Yu, J. (2005). On leverage in a stochastic volatility model. Journal of Econometrics 127:165–178.
   Web of Science ®Google Scholar
• Zhang, L., Mykland, P., Ait-Sahalia, Y. (2005). A tale of two scales: Determining integrated volatility with noisy high frequency data. Journal of the American Statistical Association 100:1394–1411.
   Web of Science ®Google Scholar