Latent state recognition by an enhanced hidden Markov model

Abstract

In this paper, we start from relaxing assumptions of traditional hidden Markov model then develop a novel framework for decoding the latent states, from which the dynamics of multivariable financial data is generated. To construct the framework, we model the observed variables as a p-order vector autoregressive process, allow the latent state to evolve through a semi-Markov chain, and shrink the auto-regression and covariance matrices via a penalized maximization likelihood method. Using the 50-dimensional simulated data, the 12-dimensional 5-minute order book data of the Chinese CSI 300 index component stocks, the 49-dimensional daily data of U.S. industry portfolio, and 1-dimensional hourly data of four primary foreign exchange rates, our empirical analyses show that the proposed model outperforms the alternative model in accurately recognizing anomalous events and achieves better sharp ratio in a pseudo trading strategy via the latent states. The superior performance is across the data frequency of minute, hour and daily, the dimension of one, 12, and 50, the data type of stock, foreign exchange rate, and industry portfolio.

Feature Selection in Jump Models

“…In the first application to real data we consider time series of financial returns. Regime-switching models are popular for inferring the state of financial markets, which is useful for both asset allocation and risk modeling purposes (Ang & Timmermann, 2012;Nystrup et al, 2019;Nystrup, Hansen et al, 2015;Yao et al, 2020). The purpose of this example is to show that jump models can be applied to data where the information separating the states comes from the second moment.…”

CNN-Based Voice Emotion Classification Model for Risk Detection

“…Markov models refer to the probability models of changes in certain phenomena. The HMM algorithm is an expansion of the Markov model with hidden states and direct observations [25]. HMM-based speed recognition estimates the parameters of a model using speed signals.…”