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Garch models in option pricing: empirical performance in the Hong Kong stock exchange
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This work project investigates on the performance of two models using stochastic volatility to price options using the Hang Seng index. The models analyzed here by are the generalized autoregressive conditional heteroskedasticity (GARCH) and the Glosten-Jagannathan-Runkle (GJR-GARCH) option pricing model. We calibrated the parameters of the models using the historical index’s log returns to price options with three different maturities. We discovered that the GJR-GARCH model ensures superior performance compared to the GARCH given its ability to capture the larger effect of negative market shocks on volatility. Moreover, both the models adequately capture the volatility smile/smirk pattern observed in the options market.
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Garch process Black-scholes Option pricing model Volatility smile