Events Calendar

Ph.D. Thesis Defence Public Lecture (DSAS) - Xing Gu

Friday, April 22, 2022
9:00 am
Virtual via Zoom

TITLE: Early-warning alert systems for financial-instability detection: An HMM-driven approach

ABSTRACT: Regulators’ early intervention is crucial when the financial system is experiencing difficulties. Financial stability must be preserved to avert banks’ bailouts, which hugely drain government's financial resources. Detecting in advance periods of financial crisis entails the development and customisation of accurate and robust quantitative techniques. The goal of this thesis is to construct automated systems via the interplay of various mathematical and statistical methodologies to signal financial instability episodes in the near-term horizon. These signal alerts could provide regulatory bodies with the capacity to initiate appropriate response that will thwart or at least minimise the occurrence of a financial crisis. This thesis presents three self-contained but related research undertakings on the subject of inventing early-warning alert systems described as follows.

Our first research study puts forward a generalised multivariate version of a hidden Markov model (HMM) that modulates the regime-switching framework. In particular, the bivariate dynamics of the Financial Stability Index (FSI) and Industrial Production Index (IPI) exhibiting salient features of stochasticity, mean reversion, seasonality, spikes and memory are accurately and simultaneously captured by the resulting HMM filters. An integrated early-warning device is constructed, where the FSI and IPI are taken as inputs, to capture both the financial and business cycles.

In our second research investigation, two different stochastic models are fused together to describe adequately the behaviours of four financial-market indices: Treasury bill yield-Eurodollar spread (TED), US Dollar Index (DXY), Volatility Index (VIX) and S&P 500 bid-ask spread, which are all deemed to mirror the liquidity levels in the financial markets. A blended multivariate HMM, which drives the regime-switching characteristics of market liquidity risk, is proposed to capture the dynamics of four time series. An early-warning signal extraction method along with its validation diagnostics is devised to generate alerts prior to or at a relatively early stage of the crisis events.

The third research work in this thesis focuses on the determination of signs for possible crisis episodes that may wreak havoc to financial market or economic stability. Synthesising stochastic-process modelling, hidden Markov filtering, Random Forest and XGBoost, we create a hybrid supervised-learning system to detect anomalies in a multivariate time-series index data. Our methodology is capable of efficiently and accurately tracing concomitantly the FSIs of multiple countries and more importantly detecting anomalous FSIs’ behaviour portending a possible financial instability. Our proposed model is able to generate dynamically 6-step-ahead binary anomalous-normal classification predictions in a probabilistic sense. Two projected anomaly-warning signals are constructed to forecast two types of extremely anomalous events in the near future with a good accuracy.

KEYWORDS: regime-switching model, HMM filtering, financial stability, change of reference measure, optimal parameter estimation, machine learning, early-warning system

Miranda Fullerton

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