2016
DOI: 10.1109/jstsp.2016.2570738
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Gaussian Process Regression Stochastic Volatility Model for Financial Time Series

Abstract: 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 regression stochastic volatility (GPRSV) model building procedures for financial time series data analysis and time-varying volatility modeling. The GPRSV ext… Show more

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Cited by 51 publications
(15 citation statements)
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“…where w(x, t) is the white random process with spectral density Sw as given by the decomposition in (2). Furthermore, A, B, and C are, in general, a matrix and vectors of linear operators, respectively, and are given through H(i ωx, i ωt).…”
Section: Conversion Of Gaussian Processesmentioning
confidence: 99%
See 1 more Smart Citation
“…where w(x, t) is the white random process with spectral density Sw as given by the decomposition in (2). Furthermore, A, B, and C are, in general, a matrix and vectors of linear operators, respectively, and are given through H(i ωx, i ωt).…”
Section: Conversion Of Gaussian Processesmentioning
confidence: 99%
“…Gaussian processes (GP) are a versatile Bayesian non-parametric modeling approach [1]. They have found widespread applications, for example in time series modeling in finance [2], meteorology [3], medical applications [4], and target tracking [5], to name a few. One well-known disadvantage is that the batch formulation in GP regression scales cubically with the number of training points.…”
Section: Introductionmentioning
confidence: 99%
“…During the previous years, researchers mostly focused on volatility modeling, evaluated as the standard deviation of an asset's return, showing the variability in Finance Time Series (FTS) data [14], which play a crucial role in effective stock forecasting. Extensive surveys were carried out showing limitations of the existing Machine Learning (ML) based stock forecasting models, based on random forest, neural networks, and support vector machines, as they proved inefficient in highly volatile market behaviors.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, GPR exhibits good adaptability and strong generalization ability in dealing with complex classification and regression problems including high dimensions, small samples, and non-linearity problems. Currently, GPR is widely and successfully applied in multiple fields such as IoT [7], time series prediction analysis [9], dynamic system model identification [10], and system control [11]. However, GPR method continues to exhibit a few deficiencies such as large calculation and limitation of the hypothesis of Gaussian noise distribution.…”
Section: Introductionmentioning
confidence: 99%