This paper presents a model for asset returns incorporating both stochastic volatility and jump e ects. The return process is driven by two types of randomness: small random shocks and large jumps. The stochastic volatility process is a ected by both types of randomness in returns. Speci cally, in the absence of large jumps, volatility is driven by the small random shocks in returns through a GARCH(1,1) model, while the occurrence of a jump event breaks the persistence in the volatility process, and resets it to an unknown deterministic level. Model estimation is performed on daily returns of S&P 500 index using the maximum-likelihood method. The empirical results are discussed. Recently, there has been fair amountofwork in the asset pricing literature that studies models with both jump and stochastic volatility dynamics. Fo
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