We investigate the relative performance of a wide array of Value at Risk (VaR) models with the daily returns of Turkish (XU100) and Croatian (CROBEX) stock index prior to and during the ongoing financial crisis. In addition to widely used VaR models, we also study the behaviour of conditional...

The purpose of this paper is to investigate the performance of VaR models at measuring risk for WTI oil one-month futures returns. Risk models, ranging from industry standards such as RiskMetrics and historical simulation to conditional extreme value model, are used to calculate commodity market...

In this paper we analyze the relative performance of 13 VaR models using daily returns of WTI, Brent, natural gas and heating oil one-month futures contracts. After obtaining VaR estimates we evaluate the statistical significance of the differences in performance of the analyzed VaR models. We...

Market risk management is one of the key factors to success in managing financial institutions. Underestimated risk can have desastrous consequences for individual companies and even whole economies, not least as could be seen during the recent crises. Overestimated risk, on the other side, may...

Currently, the financial institutions are exposed to different types of risks, which has increased the need for new analytical instruments for the risk management, being one of most developed the Value at Risk (VaR). There are different methods of calculation; however, as it was affirmed, there...

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%)...

There is an inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models since we are measuring only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the...

An inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models is that they measure only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the performance of...

The globalisation on financial markets and the development of financial derivatives has increased not only chances but also potential risk within the banking industry. Especially market risk has gained major significance since market price variation of interest rates, stocks or exchange rates...

The implementation of appropriate statistical techniques for monitoring conditional VaR models, i.e, backtesting, reported by institutions is fundamental to determine their exposure to market risk. Backtesting techniques are important since the severity of the departures of the VaR model from...