Bivariate Regime Switching FIGARCH with Maturity Effects

This paper develops a new multivariate Markov regime switching model that incorporates long memory in the volatility process. The research extends the Generalized Regime Switching (GRS) framework developed by Gray (1996) to the Bivariate case using a Fractionally Integrated GARCH process with constant correlation (B-RS_FIGARCH). The model is applied to estimate dynamic minimum variance hedge ratios for S&P 500 spot and futures prices, by focusing on the dynamics between the spot and the basis. Convergence in the basis the effect of maturity in the futures contract) is also modeled as a state dependent process. The regimes uncovered by the model can typically be associated with regimes of market sentiment (bull and bear cycles). The results also suggest that long memory dynamics exist in both states, with volatility convergence being greater in bear regimes. As a result, incorporating such effects in estimates minimum variance hedge ratios (MVHRs) can improve dynamic hedging strategies.