In this paper, using estimating function approach, a new optimal volatility estimator is introduced, and, based on the recursive form of the estimator, a data-driven generalized EWMA model for VaR forecast is proposed. An appropriate data-driven model for volatility is identified by the...

Over the last few years, Bitcoin and other cryptocurrencies have attracted the interest of many investors, practitioners and researchers. However, little attention has been paid to the predictability of their risk measures. In this paper, we compare the predictability of the one-step-ahead...

This paper studies the impact of modelling time-varying variances of stock returns in terms of risk measurement and extreme risk spillover. Using a general class of regime-dependent models, we find that volatility can be disaggregated into distinct components: a persistent stable process with...

We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we find an accelerating decline in the skewness of the conditional distributions, with significant,...

Sample covariance matrices tend to underestimate the risk of optimized portfolios. In this article, we identify special portfolios, termed “eigenportfolios,” that capture these systematic biases. Further, we present a methodology for estimating eigenportfolio biases and for adjusting the...

The purpose of this paper is to investigate whether a dynamic Value at Risk model and high frequency realized volatility models can improve the accuracy of 1-day ahead VaR forecasting beyond the performance of frequently used models. As such, this paper constructs 60 conditional volatility...