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 …

Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity …

the behaviour of returns and their volatility during both the calm as well as various crises/turmoil periods. Besides the …-GJR-GARCH) were estimated in order to examine the volatility switches of the Central European transition stock markets. The t …-distribution of error terms was used to capture the dynamics of analysed returns more precisely. The results proved high volatility …

This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and …

It is common practice to employ returns, price differences or log returns for financial risk estimation and time series … transformations. We forecasted risk (volatility) and price value and compared the results of all models using original, unmodified … prices. From the results, models showed that, on average, a logarithmic transformation achieved better volatility predictions …

monthly volatility variables, the corresponding volatility factors, and a speculation variable. In addition, the time …-of-sample forecasts. Furthermore, the speculation variable displays strong predictability for forecasting the realized volatility of …

realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically …

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 …

economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet …), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the … Malaysian tourist industry. Among them, three primarily volatility models (GARCH, EGARCH, and GJRGARCH) are used in conjunction …