statistical inference, and the simulation uses the Hamiltonian Monte Carlo (HMC) algorithm of Markov Chain Monte Carlo (MCMC) to …

predict the sector-level S&P500 exchange-traded fund (ETF) volatility. It was found that the predictive content of co-jumps is … volatility forecasting. Additionally, we carried out Monte Carlo experiments designed to examine the relative performances of the …

carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting … tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization …

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 …

market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of … to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an … the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection …

This paper investigates the volatility of daily returns on the Romanian stock market between January 2020 and April … 2021. Volatility is analyzed by means of the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest … GARCH approach. In the survey, the GARCH model (1,1) was applied to explore the volatility of the BET and BSE traded shares …

A two-stage simulation-based framework is proposed to derive Identification Robust confidence sets by applying Indirect … are treated as test statistics, which are inverted rather than optimized, via the Monte Carlo test method. Simulation …