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A key issue in the estimation of energy hedges is the hedgers’ attitude towards risk which is encapsulated in the form of the hedgers’ utility function. However, the literature typically uses only one form of utility function such as the quadratic when estimating hedges. This paper addresses...
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Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk...
Persistent link: https://www.econbiz.de/10013070500
We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using Oil futures contracts. The metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and...
Persistent link: https://www.econbiz.de/10005837212
Both in practice and in the academic literature, models for setting margin requirements in futures markets use daily closing price changes. However, financial markets have recently shown high intraday volatility, which could bring more risk than expected. Such a phenomenon is well documented in...
Persistent link: https://www.econbiz.de/10009143696
This paper applies the Extreme-Value (EV) Generalised Pareto distribution to the extreme tails of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses tail estimators from these contracts to estimate spectral risk measures, which are...
Persistent link: https://www.econbiz.de/10009143697
Mixed results have been documented for the performance of hedging strategies using futures. This paper reinvestigates this issue using an extensive set of performance evaluation metrics across seven international markets. We compare the hedging performance of short and long hedgers using...
Persistent link: https://www.econbiz.de/10009143702
This paper applies an AR(1)-GARCH (1, 1) process to detail the conditional distributions of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses the conditional distribution for these contracts to estimate spectral risk measures, which are...
Persistent link: https://www.econbiz.de/10009143703