A Class of Nonparametric Volatility Models: Applications to Financial Time Series
نویسنده
چکیده
In this paper, we first examine several volatility models in the literature. We then estimate financial volatility using multivariate adaptive regression splines (MARS) by logarithmic transformation as a preliminary analysis to examine a nonparametric volatility model. Despite its popularity, MARS has never been applied to model financial volatility. To implement the MARS methodology in a time series setting, we let the predictor variables to be lagged values which results in a model referred to as adaptive spline threshold autoregression (ASTAR). The estimation is illustrated through simulations and empirical examples. We compare the performance of the MARS volatility model with the existing parametric and nonparametric models in the literature by using several out-of-sample goodness-of-fit measures.
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