Estimating real estate value-at-risk using wavelet denoising and time series model

As the real estate market develops rapidly and is increasingly securitized, it has become an important investment asset in the portfolio design. Thus the measurement of its market risk exposure has attracted attentions from academics and industries due to its peculiar behavior and unique characteristics such as heteroscedasticity and multi scale heterogeneity in its risk and noise evolution etc. This paper proposes the wavelet denoising ARMA-GARCH approach for measuring the market risk level in the real estate sector. The multi scale heterogeneous noise level is determined in the level dependent manner in wavelet analysis. The autocorrelation and heteroscedasticity characteristics for both data and noises are modeled in the ARMA-GARCH framework. Experiment results in Chinese real estate market suggest that the proposed methodology achieves the superior performance by improving the reliability of VaR estimated upon those from traditional ARMA-GARCH approach.