Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article


Doctor of Philosophy


Statistics and Actuarial Sciences


Mamon, Rogemar

2nd Supervisor

Yu, Hao



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.

Summary for Lay Audience

The wheels of the economy, via a financial system, provide crucial services to meet the needs of households and businesses. Financial mechanisms under this system enable money borrowing for house or car purchase, having protected savings and investments for retirement, and getting valuable job trainings, amongst others. Similarly, businesses require capital to strengthen their production, expand operations, and pay their new and existing workers’ remuneration. Thus, a stable financial system is so desired as it foments and beefs up conditions for the prevention of major market-transaction disruptions. Economic participants could then raise and operate funds. However, when weaknesses in the system begin to manifest, problems can pop up and snowball if not controlled, thereby disrupting the supply of goods and services to the society.

The theme of this research is the development of an early-warning alert system (EWS) that generates short-term likelihood forecasts of financial-instability episodes. This thesis, therefore, extends the literature on dynamic modelling that identifies the economic regime characterised by the presence of threats to financial stability. Through a hidden Markov model approach in conjunction with the multivariate stochastic processes, filtering-based calibration, and machine learning techniques, three alert systems are put forward. The first EWS detects financial-stress occurrences using data related to financial and business cycles. In the second EWS, we determine the illiquidity regime, where liquidity refers to a company’s ability to pay its short-term debts and cash out its assets quickly. The market-liquidity risk is assessed by examining the joint behaviours of four financial-market indices: Treasury bill yield-Eurodollar spread, US Dollar Index, Volatility Index, and S&P 500 bid-ask spread. A hybrid methodology is proposed in the third EWS to ferret out anomalies in the joint evolution of multiple countries’ financial-stability indices.

Our EWSs support the monitoring of financial markets and structures as well as the implementation of regulators’ policy frameworks to mitigate the impact of financial fragility. This thesis aids in achieving financial stability for the efficient allocation of resources, financial-risk management, maintenance of employment rate within the neighbourhood of the economy's natural rate, and subduing of relative real or financial asset’s price movements.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Monday, April 22, 2024