Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …

This paper introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility … feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi … asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computationally simple and fast …

considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in …

variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the … forecasting technique with respect to various volatility estimators. The methodology of volatility estimation included Close …Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the …

variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the … forecasting technique with respect to various volatility estimators. The methodology of volatility estimation includes Close …Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains the …

volatility of the Standard and Poors 500 index among recent extensions of the heterogeneous autoregressive model. While we find …, improvements achieved by the inclusion of implied volatility turn out to be insignificant. …

regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns …. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach …

daily realized volatility data of Standard & Poor's 500 (S&P 500) and several other indices, we obtained good performance … heterogeneous autoregressive and other models of realized volatility. …

behavior of stock returns over a period of time. Our results show that robust volatility ratio for different k-month periods is … value robust volatility estimator with respect to the standard robust volatility estimator as proposed in the paper by … Muneer & Maheswaran (2018b). We show that the robust volatility ratio is unbiased both in the population as well as in finite …