Kwangwon Ahn

Detection and Prediction of House Price Bubbles: Evidence from a New City

In the early stages of growth of a city, housing market fundamentals are uncertain. This could attract speculative investors as well as actual housing demand. Sejong is a recently built administrative city in South Korea. Most government departments and public agencies have moved into it, while others are in the process of moving or plan to do so. In Sejong, a drastic escalation in house prices has been noted over the last few years, but at the same time, the number of vacant housing units has increased. Using the present value model, lease-price ratio, and log-periodic power law, this study examines the bubbles in the Sejong housing market. The analysis results indicate that (i) there are significant house price bubbles, (ii) the bubbles are driven by speculative investment, and (iii) the bubbles are likely to burst earlier here than in other cities. The approach in this study can be applied to identifying pricing bubbles in other cities.

Agent-based models in financial market studies

In this manuscript, we summarize prior research on the agent-based modeling of financial markets. While extensive research related to agent-based modeling has been done in various economic disciplines, we focus mainly on the evolution of the models and their applications to financial markets. A large number of studies have adopted agent-based modeling methodologies to explain various empirical findings in financial markets. Our summary shows the benefits of using such modeling to account for various financial market phenomena. We confirm that small changes in initial parameter values can lead to relatively large fluctuations through the financial markets that can be viewed as complex or chaotic systems. This also means that financial markets become volatile due to small unexpected changes in the parameters of the models that describe the market.

Forecasting Financial Crashes: Revisit to Log-Periodic Power Law

We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic analysis further demonstrates the accuracy, efficiency, and stability of our predictions.

Cash-in-Advance framework against the Quantity Theory of Money

This article revisits the Lucas illustration of the Quantity Theory of Money (QTM) and investigates whether it holds in the US monetary market. The findings confirm that QTM does not hold in the short run and the Cash-in-Advance (CIA) model fails to replicate these empirical results because the economy under the CIA framework reacts too quickly to monetary shocks. To correct for this failure, this article incorporates the financial intermediary and default as an equilibrium phenomenon into the original CIA model. The results suggest that the modified CIA model fits the short-run QTM abnormality better compared to the original model.