In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical...

The electricity price is the sensitive signal of the supply-demand balance and some other market incidents. The analysis of the price data can provide plenty of the market information. It is helpful for the participants to understand the market and improve future strategies. However, most of the...

Most financial and investment decisions are based on considerations of possible future changes and require forecasts on the evolution of the financial world. Time series and processes are the natural tools for describing the dynamic behavior of financial data, leading to the required forecasts....

The topic of volatility measurement and estimation is central to financial and more generally time series econometrics. In this paper, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In...

This paper proposes a novel and simple approach to compute daily Value at Risk (VaR) and Expected Shortfall (ES) directly from high-frequency data. It assumes that financial logarithm prices are subordinated unifractal processes in the intrinsic time, which stochastically transforms the clock...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying....