In this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective. Further, we validate the two methods using diagnostic importance sampling test procedures. Stochastic volatility models with Gaussian and Student-t distributed disturbances are considered.
Ed. by Mizrach, Bruce
4 Issues per year
IMPACT FACTOR 2011: 0.405
5-year IMPACT FACTOR: 0.739
Issues
Volume 17 (2013)
Volume 16 (2012)
Volume 15 (2011)
Volume 14 (2010)
Volume 13 (2009)
Volume 12 (2008)
Volume 11 (2007)
Volume 10 (2006)
Volume 9 (2005)
Volume 8 (2004)
Volume 7 (2003)
Volume 6 (2003)
Volume 5 (2002)
Volume 4 (2001)
Volume 3 (1999)
Volume 2 (1998)
Volume 1 (1997)
Most Downloaded Articles
- The Fiscal Cost of Financial Instability by Chiarella, Carl and Di Guilmi, Corrado
- Effects of Inflation Expectations on Macroeconomic Dynamics: Extrapolative Versus Regressive Expectations by Lines, Marji and Westerhoff, Frank
- A New Forecasting Model for USD/CNY Exchange Rate by Cai, Zongwu/ Chen, Linna and Fang, Ying
- A Nonlinear Filtering Algorithm based on Wavelet Transforms for High-Frequency Financial Data Analysis by Meinl, Thomas and Sun, Edward W.
- Forecasting Stock Market Volatility with Regime-Switching GARCH Models by Marcucci, Juri
Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
1Free University Amsterdam and Tinbergen Institute, klee@feweb.vu.nl
2Free University Amsterdam, s.j.koopman@feweb.vu.nl
Citation Information: Studies in Nonlinear Dynamics & Econometrics. Volume 8, Issue 2, Pages –, ISSN (Online) 1558-3708, DOI: 10.2202/1558-3708.1210, May 2004
Publication History:
- Published Online:
- 2004-05-18
Comments (0)