financial assets with fat tails, asymmetry, periodic behaviors in the conditional variances, and volatility clustering. The gold …

of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two … average realized volatility processes can achieve a convergence rate close to OP(n−4/9) , which is better than the convergence … based on average realized volatility processes indeed performs better than that based on the price processes. Empirically …

volatility clustering in clock-time returns, even when trade- time returns are Gaussian. Finally, we highlight conditions on the … directing process which are required in order to generate proper volatility dynamics while simultaneously matching the … unconditional distribution of returns. In-sample fitting and out-of-sample realized volatility forecasting demonstrate the strength …

Understanding the pattern of stock market volatility is important to investors as well as for investment policy …. Volatility is directly associated with risks and returns, higher the volatility the more financial market is unstable. The … volatility of the Zimbabwean stock market is modeled using monthly return series consisting of 109 observations from January 2010 …

We analyze the impact of sentiment and attention variables on volatility by using a novel and extensive dataset that … for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized … improve volatility forecasts, although the improvements are of relatively small magnitude from an economic point of view …

This work aims to investigate the (inter)relations of information arrival, news sentiment, volatility and jump dynamics … volatility modelling is justified in our specific calibration samples (2008 and 2013, respectively). However, our results reject …

massive impact on Bitcoin's realized volatility and volume. However, this effect is a recent phenomenon, which started as …