Daily returns time series usually exhibits clusters of volatility and jumps. To capture these empirical facts, many existing market models are based on jump diffusion stochastic processes for which the estimation is challenging. Indeed, some of the model features are not directly observable and difficult to disentangle. We propose a filtering approach that benefits from high frequency data available for the S&P 500 index.
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