使用資訊熵分析房價成長風險

Housing Price Growth at Risk by Information Entropy

李盈樺(Ying-Hua Li)

指導教授 ： 王泓仁

共同指導教授 ： 陳南光(Nan Kuang Chen)

國立臺灣大學/社會科學院/經濟學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202202740

摘要

本文以新型的方法資訊熵建構台灣房價風險值模型，模型設定以無條件分量迴歸做為房屋市場、條件分量迴歸歸作為房屋在金融市場的模型，兩者相減為房屋市場中潛在的不確定性，也就是房價風險。無條件分量迴歸為對傳統分量迴歸的拓展和補充。研究有關無條件分量迴歸的理論與方法正在逐漸完善中，本文旨在介紹模型並整理相關文獻。實證結果顯示，房價成長的變動具有不對稱性，且只能做部份預測。考慮到參數及非參數分配是否擬合台灣房價成長的結果，我們使用機率積分轉換 (Probability integral transform) 作為選定最佳預測的模型。最後根據結果，左尾在危機時的波動大於右尾在房價成長擴張期，我們發現此模型對於預測房價成長的下行風險相對於上行風險更具可行性。

關鍵字

房市 ； 資訊熵 ； 無條件分量迴歸 ； 條件分量迴歸 ； 成長風險

並列摘要

The thesis studies the risk of house price changes in Taiwan, particularly its downside risk of house price growth by using information entropy as the new growth-at-risk framework. We used conditional and unconditional quantile regression to evaluate the housing market, where the conditional quantile is estimated based on the financial market information. The uncertainty of the housing market was calculated by deducting the conditional quantile regression results from the unconditional quantile regression results. We used a few parametric and nonparametric methods to fit model, and used probability integral transform tests to select the optimal density function that fits Taiwan's data. We found that housing price growth risks are time-varying, asymmetric, and partly predictable. We also found that the left tail of the future housing price growth distribution is responsive during crisis while the right tail does not show responsiveness during expansions. The result indicates that the model is more capable in forecasting downside risk as opposed to the upside risk.

並列關鍵字

housing ； information entropy ； unconditional quantile regression ； conditional quantile regression ； growth at risk

參考文獻

Adrian, T., Boyarchenko, N., and Giannone, D. (2019). Vulnerable growth. American Economic Review, 109(4):1263–89.

Alter, M. A. and Mahoney, E. M. (2020). Household debt and house prices­at­risk: A tale of two countries. International Monetary Fund.

Alvarez­Ramirez, J., Rodriguez, E., and Alvarez, J. (2012). A multiscale entropy approach for market efficiency. International Review of Financial Analysis, 21:64–69.

Amisano, G. and Giacomini, R. (2007). Comparing density forecasts via weighted likeli­ hood ratio tests. Journal of Business Economic Statistics, 25(2):177–190.

Artzner, P., Delbaen, F., Eber, J.­M., and Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3):203–228.