# American Institute of Mathematical Sciences

March  2019, 1(1): 61-85. doi: 10.3934/fods.2019003

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

 Department of Statistics & Applied Probability, National University of Singapore, Singapore, 117546, SG

* Corresponding author

Published  February 2019

Fund Project: 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.

Citation: Yaxian Xu, Ajay Jasra. Particle filters for inference of high-dimensional multivariate stochastic volatility models with cross-leverage effects. Foundations of Data Science, 2019, 1 (1) : 61-85. doi: 10.3934/fods.2019003
##### References:

show all references

##### References:
Plot of Scaled Effective Sample Size (ESS) averaged over 20 runs when standard particle filter is applied to MSV model with dimension 200
Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs
Plots of mean of estimates for the $1^{st}$ component of the mean of the filters across 20 runs
Plots of SD of estimates for the $1^{st}$ component of the mean of the filters across 20 runs
Plots of SD of the estimated log-likelihoods across 20 runs
Time Comparison Study Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs
Time Comparison Study Plots of Relative SD of the estimated log-likelihoods (w.r.t. the SD of the STPF) across 20 runs
Trace plots, histograms and ACF plots of the parameter estimates using PMMH for $\rho_{ij,\varepsilon\varepsilon}$
Trace plots, histograms and ACF plots of the parameter estimates using PMMH for $\sigma_{i,\eta\eta}$
Plot of Scaled Effective Sample Size (ESS)
Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs
Plots of SD of the estimated log-likelihoods across 20 runs
Time Comparison Study Plots of Scaled Effective Sample Size (ESS) averaged over 20 runs
Time Comparison Study Plots of Relative SD of the estimated log-likelihoods (w.r.t. the SD of the STPF) across 20 runs
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.
 $d$ 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.
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.
 $d$ 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.
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 $N=40000$ $N=50$, $M_d=20$ $120$
 $d$ Standard PF STPF Computation Time 100 $N=25000$ $N=50$, $M_d=20$ $16.7$ 200 $N=40000$ $N=50$, $M_d=20$ $120$
 [1] Olli-Pekka Tossavainen, Daniel B. Work. Markov Chain Monte Carlo based inverse modeling of traffic flows using GPS data. Networks & Heterogeneous Media, 2013, 8 (3) : 803-824. doi: 10.3934/nhm.2013.8.803 [2] Pierre Degond, Simone Goettlich, Axel Klar, Mohammed Seaid, Andreas Unterreiter. Derivation of a kinetic model from a stochastic particle system. Kinetic & Related Models, 2008, 1 (4) : 557-572. doi: 10.3934/krm.2008.1.557 [3] Yuezheng Gong, Jiaquan Gao, Yushun Wang. High order Gauss-Seidel schemes for charged particle dynamics. Discrete & Continuous Dynamical Systems - B, 2018, 23 (2) : 573-585. doi: 10.3934/dcdsb.2018034 [4] Annalisa Pascarella, Alberto Sorrentino, Cristina Campi, Michele Piana. Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms. Inverse Problems & Imaging, 2010, 4 (1) : 169-190. doi: 10.3934/ipi.2010.4.169 [5] Guillaume Bal, Ian Langmore, Youssef Marzouk. Bayesian inverse problems with Monte Carlo forward models. Inverse Problems & Imaging, 2013, 7 (1) : 81-105. doi: 10.3934/ipi.2013.7.81 [6] Giacomo Dimarco. The moment guided Monte Carlo method for the Boltzmann equation. Kinetic & Related Models, 2013, 6 (2) : 291-315. doi: 10.3934/krm.2013.6.291 [7] Robert J. Elliott, Tak Kuen Siu. Stochastic volatility with regime switching and uncertain noise: Filtering with sub-linear expectations. Discrete & Continuous Dynamical Systems - B, 2017, 22 (1) : 59-81. doi: 10.3934/dcdsb.2017003 [8] Michele Gianfelice, Marco Isopi. On the location of the 1-particle branch of the spectrum of the disordered stochastic Ising model. Networks & Heterogeneous Media, 2011, 6 (1) : 127-144. doi: 10.3934/nhm.2011.6.127 [9] Lee DeVille, Nicole Riemer, Matthew West. Convergence of a generalized Weighted Flow Algorithm for stochastic particle coagulation. Journal of Computational Dynamics, 2019, 6 (1) : 69-94. doi: 10.3934/jcd.2019003 [10] Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095 [11] Jiakou Wang, Margaret J. Slattery, Meghan Henty Hoskins, Shile Liang, Cheng Dong, Qiang Du. Monte carlo simulation of heterotypic cell aggregation in nonlinear shear flow. Mathematical Biosciences & Engineering, 2006, 3 (4) : 683-696. doi: 10.3934/mbe.2006.3.683 [12] Michael B. Giles, Kristian Debrabant, Andreas Rössler. Analysis of multilevel Monte Carlo path simulation using the Milstein discretisation. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 3881-3903. doi: 10.3934/dcdsb.2018335 [13] Ralf Banisch, Carsten Hartmann. A sparse Markov chain approximation of LQ-type stochastic control problems. Mathematical Control & Related Fields, 2016, 6 (3) : 363-389. doi: 10.3934/mcrf.2016007 [14] Laura Martín-Fernández, Gianni Gilioli, Ettore Lanzarone, Joaquín Míguez, Sara Pasquali, Fabrizio Ruggeri, Diego P. Ruiz. A Rao-Blackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predator-prey system. Mathematical Biosciences & Engineering, 2014, 11 (3) : 573-597. doi: 10.3934/mbe.2014.11.573 [15] Seung-Yeal Ha, Doron Levy. Particle, kinetic and fluid models for phototaxis. Discrete & Continuous Dynamical Systems - B, 2009, 12 (1) : 77-108. doi: 10.3934/dcdsb.2009.12.77 [16] Nicolas Fournier. Particle approximation of some Landau equations. Kinetic & Related Models, 2009, 2 (3) : 451-464. doi: 10.3934/krm.2009.2.451 [17] David Cowan. Rigid particle systems and their billiard models. Discrete & Continuous Dynamical Systems - A, 2008, 22 (1&2) : 111-130. doi: 10.3934/dcds.2008.22.111 [18] Joseph Nebus. The Dirichlet quotient of point vortex interactions on the surface of the sphere examined by Monte Carlo experiments. Discrete & Continuous Dynamical Systems - B, 2005, 5 (1) : 125-136. doi: 10.3934/dcdsb.2005.5.125 [19] Chjan C. Lim, Joseph Nebus, Syed M. Assad. Monte-Carlo and polyhedron-based simulations I: extremal states of the logarithmic N-body problem on a sphere. Discrete & Continuous Dynamical Systems - B, 2003, 3 (3) : 313-342. doi: 10.3934/dcdsb.2003.3.313 [20] Mazyar Zahedi-Seresht, Gholam-Reza Jahanshahloo, Josef Jablonsky, Sedighe Asghariniya. A new Monte Carlo based procedure for complete ranking efficient units in DEA models. Numerical Algebra, Control & Optimization, 2017, 7 (4) : 403-416. doi: 10.3934/naco.2017025

Impact Factor:

## Tools

Article outline

Figures and Tables