Forecasts of crude oil prices' volatility are important inputs to many decision making processes in application areas such as macroeconomic policy making, risk management, options pricing, and portfolio management. Despite the fact that a large number of forecasting models have been designed to...

The consistent ranking of multivariate volatility models by means of statistical loss function is a challenging research field, because it concerns the quality of the proxy chosen to replace the unobserved volatility, the set of competing models to be ranked and the kind of loss function. The...

We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized...

We emphasize the role of news-based economic policy and equity market uncertainty indices as robust drivers of oil price fluctuations. In that, we utilizea new hybrid nonparametric quantile causality methodology in order to investigate whether EPU and EMU uncertainty measures incorporate...

Abstract Behavioural finance has challenged many claims of efficient market hypothesis (EMH). Unfortunately many of these challenges are in the form of anecdotal evidence and lack quantification. This article uses market data together with some simple statistics to show that in practice certain...

This paper investigates volatility spillover across crude oil market and wheat and corn markets. The corn commodity is taken here to assess the impact of change in demand for biofuel on wheat market. Results of multivariate GARCH model show evidence of corn price volatility transmission to wheat...

Asset allocation and risk calculations depend largely on volatile models. The parameters of the volatility models are estimated using either the Maximum Likelihood (ML) or the Quasi-Maximum Likelihood (QML). By comparing the out-of-sample forecasting performance of 68 ARCH-type models using...

The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the...

The Great Recession endured by the main industrialized countries during the period 2008–2009, in the wake of the financial and banking crisis, has pointed out the major role of the financial sector on macroeconomic fluctuations. In this respect, many researchers have started to reconsider the...

A multivariate Markov-switching ARCH (MVSWARCH) model in which variance/correlations for futures and spot returns is controlled by a state-varying mechanism is introduced and used to design a state-varying stock index futures hedge ratio. Additionally, a conventional random-variance framework,...