This article explores the relationships between several forecasts for the volatility built from multi-scale linear ARCH processes, and linear market models for the forward variance. This shows that the structures of the forecast equations are identical, but with different dependencies on the...

Value at Risk (VaR) has emerged in recent years as a standard tool to measure and control the risk of trading portfolios. Yet, existing theoretical analyses of the optimal behavior of a trader subject to VaR limits have produced a negative view of VaR as a risk-control tool. In particular, VaR...

The quality of operational risk data sets suffers from missing or contaminated data points. This may lead to implausible characteristics of the estimates. Outliers, especially, can make a modeler's task difficult and can result in arbitrarily large capital charges. Robust statistics provides...

The Basel II framework allows the calculation of the capital requirements for market risk with Value-at-Risk models. Since no special model is prescribed in the framework, banks may use simple models with questionable assumptions concerning their underlying distributions. Our numerical analysis...

It is well-established that equity returns are not Normally distributed, but what should the portfolio manager do about this, and is it worth the effort? It is now feasible to employ better multivariate distribution families that capture heavy tails and skewness in the data; we argue that among...

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to 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...

This work investigates the performance of different models of value at risk. We include several methods (parametric, historical simulation, Monte Carlo, and extreme value theory) and some models to compute the conditional variance. We analyze several international stock indexes and examine two...

Purpose – This study aims to examine the stock returns distributions in ten countries in the periods before and after the global financial crisis (GFC) to evaluate how well the empirical distributions conformed to the extreme value theory (EVT) which underlies a family of risk management...

Seeing the firm as a nexus of activities and projects, we propose a characterization of the firm where variations in the market price of risk should induce adjustments in the firm's portfolio of projects. In a setting where managers disagree with respect to what investment maximizes value,...