This paper proposes a procedure for using a GARCH or exponentially weighted moving average model in conjunction with historical simulation when computing value at risk. It involves adjusting historical data on each market variable to reflect the difference between the historical volatility of the market variable and its current volatility. We compare the approach using nine years of daily data ...

Transition matrices are an important determinant for risk management and VaR calculations in credit portfolios. It is well known that rating migration behavior is not constant through time. It shows cyclical behavior and significant changes over the years. We investigate the effect of changes in migration matrices on credit portfolio risk in terms of Expected Loss and Value-at-Risk figures for ...

1 VaR (Value at Risk) estimates are currently based on two main techniques, the variance-covariance approach or simulation. Statistical and computational problems affect the reliability of these techniques. We illustrate a new technique, filtered historical simulation, that is designed to remedy some of the shortcomings of the simulation approach. We compare the estimates it produces with tradi...

1 Hedging is an important topic for both financial practice and theory. The rational of hedging and the optimal hedging ratio is examined by many papers, but the choice of hedging instrument is much less investigated, or restricted to options and futures. In this paper we analyze different hedging strategies from the aspect of Hungarian exporters with a long euro position. We evaluate each stra...

Timeand state-domain methods are two common approaches to nonparametric prediction. Whereas the former uses data predominantly from recent history, the latter relies mainly on historical information. Combining these two pieces of valuable information is an interesting challenge in statistics. We surmount this problem by dynamically integrating information from both the time and state domains. T...

It is difficult to compute Value-at-Risk (VaR) using multivariate models able to take into account the dependence structure between large numbers of assets and being still computationally feasible. A possible procedure is based on functional gradient descent (FGD) estimation for the volatility matrix in connection with asset historical simulation. Backtest analysis on simulated and real data pr...

In this paper, we show that although minimum-variance hedging unambiguously reduces the standard deviation of portfolio returns, it tends to increase portfolio kurtosis and consequently the effectiveness of hedging in terms of a more general measure of risk such as VaR is uncertain. We compare the reduction in standard deviation with the reduction in 99% VaR for thirteen cross-hedged currency p...

Introduction A backtest is a historical simulation of an algorithmic investment strategy. Among other things, it computes the series of profits and losses that such strategy would have generated had that algorithm been run over that time period. Popular performance statistics, such as the Sharpe ratio or the Information ratio, are used to quantify the backtested strategy’s return on risk. Inves...