Article Contents
Article Contents

# Particle filters for inference of high-dimensional multivariate stochastic volatility models with cross-leverage effects

• * Corresponding author

AJ is supported by an AcRF tier 2 grant: R-155-000-161-112

• Multivariate stochastic volatility models are a popular and well-known class of models in the analysis of financial time series because of their abilities to capture the important stylized facts of financial returns data. We consider the problems of filtering distribution estimation and also marginal likelihood calculation for multivariate stochastic volatility models with cross-leverage effects in the high dimensional case, that is when the number of financial time series that we analyze simultaneously (denoted by $d$) is large. The standard particle filter has been widely used in the literature to solve these intractable inference problems. It has excellent performance in low to moderate dimensions, but collapses in the high dimensional case. In this article, two new and advanced particle filters proposed in [4], named the space-time particle filter and the marginal space-time particle filter, are explored for these estimation problems. The better performance in both the accuracy and stability for the two advanced particle filters are shown using simulation and empirical studies in comparison with the standard particle filter. In addition, Bayesian static model parameter estimation problem is considered with the advances in particle Markov chain Monte Carlo methods. The particle marginal Metropolis-Hastings algorithm is applied together with the likelihood estimates from the space-time particle filter to infer the static model parameter successfully when that using the likelihood estimates from the standard particle filter fails.

Mathematics Subject Classification: Primary: 82C80; Secondary: 60G99, 62F15.

 Citation:

• Figure 1.  Plot of Scaled Effective Sample Size (ESS) averaged over 20 runs when standard particle filter is applied to MSV model with dimension 200

Figure 2.  Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs

Figure 3.  Plots of mean of estimates for the $1^{st}$ component of the mean of the filters across 20 runs

Figure 4.  Plots of SD of estimates for the $1^{st}$ component of the mean of the filters across 20 runs

Figure 5.  Plots of SD of the estimated log-likelihoods across 20 runs

Figure 6.  Time Comparison Study Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs

Figure 7.  Time Comparison Study Plots of Relative SD of the estimated log-likelihoods (w.r.t. the SD of the STPF) across 20 runs

Figure 8.  Trace plots, histograms and ACF plots of the parameter estimates using PMMH for $\rho_{ij,\varepsilon\varepsilon}$

Figure 9.  Trace plots, histograms and ACF plots of the parameter estimates using PMMH for $\sigma_{i,\eta\eta}$

Figure 10.  Plot of Scaled Effective Sample Size (ESS)

Figure 11.  Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs

Figure 12.  Plots of SD of the estimated log-likelihoods across 20 runs

Figure 13.  Time Comparison Study Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs

Figure 14.  Time Comparison Study Plots of Relative SD of the estimated log-likelihoods (w.r.t. the SD of the STPF) across 20 runs

Table 1.  Number of particles used in each algorithm

 $d$ Standard PF STPF Marginal STPF 25 $N=1000$ $N=50$, $M_d=20$ $N=1$, $M_d=1000$ 50 $N=1000$ $N=50$, $M_d=20$ $N=1$, $M_d=1000$ 100 $N=1000$ $N=50$, $M_d=20$ N.A. 200 $N=1000$ $N=50$, $M_d=20$ N.A.

Table 2.  Computation time (in minutes) per 50 time points for each algorithm

 $d$ Standard PF STPF Marginal STPF 25 $0.3$ $2$ $46$ 50 $0.6$ $3$ $110$ 100 $2$ $16.7$ N.A. 200 $5$ $120$ N.A.

Table 3.  Number of particles and computation time (in minutes) per 50 time points for each algorithm

 $d$ Standard PF STPF Computation Time 100 $N=25000$ $N=50$, $M_d=20$ $16.7$ 200 $N=40000$ $N=50$, $M_d=20$ $120$
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