QML- и MCMC-методы оценивания параметров модели стохастической волатильности: применение к российскому рынку ценных бумаг

This study compares two estimation approaches for the stochastic volatility (SV) model, namely quasi-maximum likelihood (QML) and Markov chain Monte Carlo (MCMC). A simulation exercise shows that MCMC delivers markedly more accurate parameter estimates than QML. However, QML remains substantially faster and far less computationally demanding, which makes it attractive in large-scale empirical applications. To the best of our knowledge, this is the first study to estimate an SV model using Russian stock market data. To fill this gap, we examine securities included in the Moscow Exchange IMOEX index over the period from 1 January 2015 to 1 February 2022. For each stock that was a constituent of the IMOEX as of 1 February 2022 and had at least three years of trading history, SV model parameters are estimated using both QML and MCMC methods. The resulting estimates are used to rank stocks by volatility and to classify them into low-, medium-, and high-volatility groups. Economic sectors are also grouped according to their volatility profiles. The empirical results further indicate that QML-based estimation systematically understates firm-level risk, which may lead to sizable losses in portfolio management. The work may be of interest to specialists in financial econometrics, as well as macroeconomists and hedge fund employees.