This paper investigates the volatility spillovers of four major equity markets using a new approach namely, the Filtered Historical Simulation approach (FHS). The FHS captures very effectively the changes ...

Abstract The objective of this chapter is to examine the ARCH family of volatility models and its use in risk analysis and measurement. An overview of unconditional and conditional volatility models is provided. The former ...

This paper we backtest the FHS VaR model on three types of portfolios invested over a period of two years. The first set of backtests consists of LIFFE financial futures and options contracts traded on LIFFE. In the second ...

In this paper we propose to model short-term interest rates by taking into consideration both the asymmetric properties of returns, using Pearson’s type IV distribution, and the time-varying volatility, using GARCH models. ...

We propose filtering historical simulation by GARCH processes to model the future distribution of assets and swap values. Options’ price changes are computed by full reevaluation on the changing prices of underlying assets. ...

Filtered historical simulation provides the general framework to our backtests of portfolios of derivative securities held by a large sample of financial institutions. We allow for stochastic volatility and exchange rates. ...

This paper describes and applies a nonparametric model for pricing multivariate contingent claims. Multivariate contingent claims are contracts whose payoffs depend on the future prices of more than one underlying variable. ...

This paper describes and analyses the use of the Filtered Historical Simulation algorithm in pricing spread options. Spread options are contracts whose payoff depends on the price difference (spread) between two or more ...

Recent studies have strongly criticised conventional VaR models for not providing a coherent risk measure. Acerbi provides the intuition for an entire family of coherent measures of risk known as “spectral risk measures” ...

Value at Risk (VaR) is increasingly popular as a management and regulatory tool. To further its acceptance it is necessary to assess its reliability under conditions likely to be encountered in financial markets. A logical ...