Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation. In order to simplify the …

The paper proposes a general asymmetric multifactor Wishart stochastic volatility (AMWSV) diffusion process which …

volatility models. We propose a continuous timefractionally integrated Wishart stochastic volatility (FIWSV) process. We derive …

conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain … variance, in the order of integration, in the short memory characteristics and in the volatility of volatility …

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this … stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value …

. The sampling methods are flexible, and this advantage is used to extend the model to incorporate a stochastic volatility … process. The volatility changes both in the Nile data, and for comparison also in daily S&P 500 return data, are investigated …

conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain … variance, in the order of integration, in the short memory characteristics and in the volatility of volatility. …