In this article we present a theoretical review of the existing literature on Value at Risk (VaR)
specifically focussing on the development of new approaches for its estimation. We effect a …

The interest in forecasting the Value at Risk (VaR) has been growing over the last two
decades, due to the practical relevance of this risk measure for financial and insurance …

Based on high-frequency data for more than fifty commodities, currencies, equity indices,
and fixed-income instruments spanning more than two decades, we document strong …

We use the information in intraday data to forecast the volatility of crude oil at a horizon of 1–
66 days using a variety of models relying on the decomposition of realized variance in its …

We evaluate the predictive power of leading indicators for output growth at horizons up to 1
year. We use the MIDAS regression approach as this allows us to combine multiple …

A complete guide to the theory and practice of volatility models in financial engineering
Volatility has become a hot topic in this era of instant communications, spawning a great …

In this paper we address the issue of forecasting Value–at–Risk (VaR) using different
volatility measures: realized volatility, bipower realized volatility, two-scales realized …

We introduce the scaled principal component analysis (sPCA) method to forecast oil
volatility, and compare it with two commonly used dimensionality reduction methods …

This paper investigates the price volatility interaction between the crude oil and equity
markets in the US using 5-min data over the period 2009–2012. Our main findings can be …

We develop a discrete-time stochastic volatility option pricing model exploiting the
information contained in the Realized Volatility (RV), which is used as a proxy of the …