Jianan Han

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In this paper, we propose a novel nonparametric modeling framework for financial time series data analysis, and apply it to the problem of time varying volatility modeling. Existing parametric models have a rigid-form transition function and they often have over-fitting problems when model parameters are estimated using maximum likelihood methods. These(More)
Traditional economic models have rigid-form transition functions when modeling time-varying volatility of financial time series data and cannot capture other time-varying dynamics in the financial market. In this paper, combining the Gaussian process state-space model framework and the stochastic volatility (SV) model, we introduce a new Gaussian process(More)
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