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Value-at-risk bounds for multivariate heavy tailed distribution: an application to the Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity model
This paper aims to derive VaR bounds for the portfolios of possibly dependent financial assets for heavy tailed Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity processes using extreme value theory copulas.
Hardwiring of older risk measure into Priips means risk ratings could mislead investors
Investment Association welcomes suggestions to regulate illiquid and levered funds
This paper studies the impact of model risk on EVT methods when determining the value-at-risk and expected shortfall.
Bids to use bigger datasets give no better loss forecasts, says hedge fund
Wujiang Lou extends liability-side pricing theory to initial margin
The papers in this issue are all related to energy risk management, including both risk assessment and risk hedging by financial derivatives.
Rama Cont and Lakshithe Wagalath introduce a liquidation-adjusted VAR
Value-at-risk estimation with the Carr–Geman–Madan–Yor process: an empirical study on foreign exchange rates
This paper investigates the performance of the CGMY distribution in estimating the risk of FX rates.
Testing value-at-risk models in emerging markets during crises: a case study on South Eastern European countries
This paper examines the applicability of a wide range of VaR models in emerging markets, focusing on South Eastern European countries.
This paper investigates whether there are existing common model features that yield consistently superior results under both VaR and ES risk metrics in the energy commodities markets.
Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis
This paper models the tail behavior of daily returns and forecasting VaR in order to evaluate the performance of several skewed and symmetric distributions.
This paper looks at hourly spot prices at the German electricity market and applies extreme value theory (EVT) to investigate the tails of the price change distribution.
A new method to estimate marginal VAR and marginal ES is presented
This paper discusses a VaR time-scaling approach based on fitting a distribution function so as to apply a Monte Carlo simulation to determine long-term VaR.
Alessandro Mauro shows how using value-at-risk can improve market risk analysis in the energy sector