small minority of different cases. Investigating further we find that all volatility series show persistence breaks during …

framework is a bivariate volatility model, where volatility spillovers of either positive or negative sign are allowed for. Our … countries. Regarding the volatility spillovers, such spillovers from bond returns to those of stocks are stronger than the other … results show that by considering time-varying return and volatility spillovers when calculating the risk-minimising portfolio …

We present a two-factor volatility model to study the impact of news arrival and trading volume on stock returns … variance. The model can explicitly account for the association between volatility and volume, as well as the persistence in … volatility variations. The common observation that large volumes are associated with high volatility is explained by the fact …

It is generally believed that excessive stock market volatility reflects non-mathematical market expectations that are …

The relationship between the level of stock market volatility and public information flow is non-linear, resembling a … bell-shaped function. Medium levels of information flow generate heightened volatility, whereas weak and strong information … realized GARCH model with time-varying intercept, measuring changes in the overall volatility level, which is governed by a new …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

volatility forecasts leads to mean-variance portfolios with higher risk-adjusted performance in terms of Sharpe ratio as well as …

This paper studies the pitfalls of applying the Cholesky decomposition for forecasting multivariate volatility. We …

This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a … forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the … influence of different timescales on volatility forecasts. The decomposition of volatility into several timescales approximates …