ARC Grant Success

The project aims to develop and construct a measure of systemic risk for national real-estate markets in Australia, and its main trading partners; China, Japan, New Zealand, United Kingdom and US.  It will investigate how real estate risks migrate geographically over time and during periods of financial turbulence.

Early detection of the onset of future housing bubble collapses would be of significant benefit to policy makers, Australia’s trading partners, the real estate industry and ultimately home buyers.  New methodology has been developed and is intended to become part of a market stability surveillance program that can assess the impact of real-estate risk on the overall economy.

In a second ARC Discovery Grant, CFR member Professor Ken Siu has received $450,000 to establish a novel field: Two-price quantitative finance, and explore its applications. The new field will integrate two major schools for modelling and explain the presence of two prices, the buying and selling prices, widely observed in real-world markets, and the equilibrium approach from the fundamental law of one price.

The aim of the project is to deepen the understanding of fundamental relationships between liquidity, prices, risk and the economy. It is expected to make a long-term impact on quantitative finance and related applications through providing a deep understanding of, and a new perspective for, the design, risk and fairness of finance, property and insurance markets.

The third project is an ARC Discovery Early Career Researcher Grant (DECRA) that has been awarded to Associate Professor Shuping Shi for monitoring financial bubbles using high-frequency data. This project, which received $375,000, aims to develop an econometric procedure for monitoring speculative behaviour, often labelled as bubbles, in financial markets.

Financial speculation can inflict harm on the real economy, and crises or recessions are often preceded by excessive asset market speculation. The project will utilise intraday information for bubble detection and address major technical challenges arising from high-frequency financial data.

It is expected to significantly improve the speed and accuracy of bubble detection which will provide more timely and precise warning alerts for investment decisions, market surveillance and policy action, enhancing risk management tools in the economy.