Análisis de las estrategias de cobertura basadas en un modelo de regresión cuantílica multiescala
DOI:
https://doi.org/10.21919/remef.v21i4.1055Keywords:
Emerging futures markets, multiscale quantile hedging ratios, multiscale hedging models, , wavelet analysisAbstract
Effectiveness of hedging strategies based on a multiscale quantile regression model
This paper aims to combine the wavelet theory and the quantile regression method to estimate multiscale quantile hedging ratios (MQHRs) and examine the hedging effectiveness at different time scales for Bovespa and S&P/BMV IPC stock indices futures mar for portfolios of the Bovespa and S&P/BMV IPC stock indices. The results show that the MQHRs and the hedging effectiveness increase as the hedge horizon increases. According to the variance and VaR reduction measures, the results indicate that the performance of the multiscale QR model has comparative advantages over the multiscale OLS model in improving the in-sample and out-of-sample hedging effectiveness, particularly for S&P/BMV IPC stock index futures. Therefore, the multiscale QR model has better predictive properties to reduce the base risk and the excess residual tail risk for the hedge portfolio. The findings have implications for the efficient capital allocation of risk-averse investors and the regulation over the investment rules of pension funds in Brazil and Mexico.
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