memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the …

(MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset …

This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets … (Brent oil and WTI oil) for the period 2011-2021. The main objective is to make step ahead volatility predictions for the … important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for …

Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and … the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper …, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an …

This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in … the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms … dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage …

range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility … the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies …

heterogeneous information present in the financial market and the long memory of volatility. Some theoretical properties of this …