A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation

Among the models that describe the dynamic behaviors of financial market, the discrete time microstructure model stands out because of its efficiency in considering the relationship between the price, excess demand, and liquidity of a market. However, the estimation problem of such a microstructure model is challenging, because the model is essentially a nonlinear state space model. A decent solution is to define a self-organizing state-space model by combining the unknown parameters and the state vector of the original model into a new state vector. Then, the sequential Monte Carlo method can be used to simultaneously estimate the parameters and states. To handle the difficulty in setting the initial distributions of parameters for the self-organizing state space model, we propose to use the results obtained by the Kalman filter on the original microstructure model. Finally, a dynamic asset allocation strategy is designed based on estimated excess demand using the self-organizing state space model. The proposed methodology is evaluated by the China SZSE (ShenZhen Stock Exchange) Composite Index time series, and the results show its effectiveness.

[1]  S. Ross,et al.  The valuation of options for alternative stochastic processes , 1976 .

[2]  Stephen L Taylor,et al.  MODELING STOCHASTIC VOLATILITY: A REVIEW AND COMPARATIVE STUDY , 1994 .

[3]  Steven Kou,et al.  A Jump Diffusion Model for Option Pricing , 2001, Manag. Sci..

[4]  G. Kitagawa.  A self-organizing state-space model , 1998 .

[5]  Genshiro Kitagawa,et al.  A modeling approach to financial time series based on market microstructure model with jumps , 2015, Appl. Soft Comput..

[6]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[7]  Dietrich Stauffer,et al.  A generalized spin model of financial markets , 1999 .

[8]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[9]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[10]  William N. Goetzmann,et al.  Pairs Trading: Performance of a Relative Value Arbitrage Rule , 1998 .

[11]  J. T. Spooner,et al.  Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .

[12]  G. Kitagawa.  A nonlinear smoothing method for time series analysis , 1991 .

[13]  Xiaohong Chen,et al.  An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure model , 2016, Appl. Soft Comput..

[14]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[15]  Rama Cont,et al.  A Langevin approach to stock market fluctuations and crashes , 1998 .

[16]  M. D. Johnson,et al.  Excess Demand Financial Market Model , 2002 .

[17]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[18]  R. C. Merton,et al.  Theory of Rational Option Pricing , 2015, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[19]  T. Ozaki,et al.  Use of Stochastic Differential Equation Models in Financial Time Series Analysis : Monitoring and Control of Currencies in Exchange Market , 2003 .

[20]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[21]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[22]  R. C. Merton,et al.  Option pricing when underlying stock returns are discontinuous , 1976 .

[23]  Liu Min-an.  Dynamic Asset Allocation Control Based on Estimated Excess Demand , 2009 .

[24]  Hans R. Künsch,et al.  Approximating and Maximising the Likelihood for a General State-Space Model , 2001, Sequential Monte Carlo Methods in Practice.

[25]  M. Yor,et al.  The Fine Structure of Asset Retums : An Empirical Investigation ' , 2006 .

[26]  Tohru Ozaki,et al.  Modeling and asset allocation for financial markets based on a discrete time microstructure model , 2003 .

[27]  Yoshiyasu Tamura,et al.  Modelling and asset allocation for financial markets based on a stochastic volatility microstructure model , 2005, Int. J. Syst. Sci..