In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation's distribution is estimated with the fully...

In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information...

In the present paper we examine whether financial markets could have helped predict exchange rates in three selected Central and Eastern European (CEE) economies of the EU, namely the Czech Republic, Hungary and Poland, during the current financial crisis. To this end, we derive risk-neutral...

This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

In the present paper we examine whether financial markets could have helped predict exchange rates in three selected Central and Eastern European (CEE) economies of the EU, namely the Czech Republic, Hungary and Poland, during the current financial crisis. To this end, we derive risk-neutral...

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the quadratic variation of financial prices. This estimator was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We...

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to one month. We extend the methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns. Considering...

This paper constructs a monthly real-time oil price dataset using backcasting and compares the forecast performance of alternative models of constant and time-varying volatility based on the accuracy of point and density forecasts of real oil prices of both real-time and ex-post revised data....

We propose a methodology to employ high frequency financial data to obtain estimates of volatility of log-prices which are not affected by microstructure noise and Lévy jumps. We introduce the 'number of jumps' as a variable to explain and predict volatility and show that the number of jumps in...