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About Financial Time Series Volatility Analysis Using Gaussian Process State-Space Models
This thesis proposes a novel nonparametric modeling framework called Gaussian Process Regression Stochastic Volatility (GPRSV) models for analyzing financial time series data. The GPRSV models extend standard state-space models by adding a Gaussian process prior to the hidden state transition process. Both online particle filtering and offline particle Markov chain Monte Carlo methods are studied for learning the proposed GPRSV models from simulated and empirical financial data. The thesis demonstrates the GPRSV framework and inference methods can provide more flexible modeling of time-varying volatility compared to existing parametric volatility models.
Detailed Information
| Author: | ['Federico Dragoni'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 68 |
| Language: | English |
| Format: | |
| Price: | FREE |
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