Distribution Forecasting in Nonlinear Models with Stochastic Volatility
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چکیده
This paper investigates the effect of the market return on the value of systematic risk using a semiparametric multivariate GARCH model. We nonparametrically estimate the dynamic conditional beta without any restrictive assumption on the joint density of the data. This model captures movements in systematic risk over time, and we find that the time-varying beta of a stock nonlinearly depends on the contemporaneous value of excess market returns. The model is extended to allow nonlinear dependence in Fama-French factors. In general, in highly volatile markets, beta is almost constant, while in stable markets, the beta coefficient can be highly and asymmetrically dependent on the value of the market excess return.
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