An automated financial indices-processing scheme for classifying market liquidity regimes

Abstract

A multivariate hidden Markov model (HMM)-based approach is developed to capture simultaneously the regime-switching dynamics of four financial market indicators: Treasury-Euro Dollar rate spread, US dollar index, volatility index and S&P 500 bid-ask spread. These indicators exhibit stochasticity, mean reversion, spikes and state memory, and they are deemed to drive the main characteristics of liquidity risk and regarded to mirror financial markets' liquidity levels. In this paper, an online system is proposed in which observed indicators are processed and the results are then interfaced with an advanced alert mechanism that gives out appropriate measures. In particular, two stochastic models, with HMM-modulated parameters switching between liquidity regimes, are integrated to capture the evolutions of the four time series or their transformations. Parameter estimation is accomplished by deriving adaptive multivariate filters. Indicators' joint empirical characteristics are captured well and useful early warnings are obtained for occurrence prediction of illiquidity episodes.

Keywords:

• Multivariate HMM filtering
• optimal parameter estimation
• S&P 500
• TED
• VIX
• DXY

Acknowledgments

R. Mamon expresses his sincere appreciation for the hospitality of the Division of Physical Sciences and Mathematics, University of the Philippines Visayas, where certain parts of this paper were drafted during an academic visit.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the Natural Sciences and Engineering Research Council of Canada through R. Mamon's Discovery Grant (RGPIN-2017-04235).