We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important...

A model of portfolio return dynamics is considered in which the price of risk is permitted to be heterogeneous. In doing this, a novel method is proposed that delivers improved out-of-sample forecasts of portfolio returns. The main innovation is the use of a set of predictors that account for...

In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as “anomalies” the theory of rational finance cannot explain: (i) Predictability of asset returns; (ii) The Equity Premium; (iii) The Volatility Puzzle. We offer resolutions of...

Despite the vast academic literature on modelling stochastic volatility, many finance practitioners still use the simple "RiskMetrics" approach of J. P. Morgan (1997), based on the exponentially weighted moving average (EWMA) volatility combined with the $\sqrt{h}$-rule for scaling volatility...

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 limitations. HF data feature microstructure problem,...

This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...