The Markov-switching Multifractal model of asset returns with Student-t innovations (MSM-t
henceforth) is introduced as an extension to the Markov-switching Multifractal model of asset …

Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of
asset returns, while maintaining conceptual simplicity. The commonly made assumption of …

A new robust stochastic volatility (SV) model having Student‐t marginals is proposed. Our
process is defined through a linear normal regression model driven by a latent gamma …

This paper analyses the stochastic volatility models induced by non‐negative Markov
sequences generated by product autoregressive models. In particular, a stationary …

The market microstructure (MM) models using normal distribution are useful tools for
modeling financial time series, but they cannot explain essential characteristics of skewness …

This paper explores the exponential stochastic volatility model generated by first-order
exponential Markov sequences to model financial time series. The stationary exponential …

This paper develops a novel and efficient algorithm for Bayesian inference in inverse
Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it …

Normal mixture of inverse Gaussian known as Normal-inverse Gaussian distribution is well-
known in the context of modeling the stochastic volatility. In the present work, an …

Stochastic volatility (SV) models provide a natural framework for a representation of time
series for financial asset returns. As a result, they have become increasingly popular in the …