The impact of infectious diseases on hotel occupancy rate based on independent component analysis.

The recent global outbreak of Influenza A (H1N1), or the more commonly known as swine flu, has negatively affected the tourism and hospitality industries in many countries. This article reports a study that applied independent component analysis, a novel statistical technique, to separate the dominant factors which determine the levels of hotel occupancy rates in Hong Kong. Empirical findings would provide useful insights on how the dynamic lodging demand reacts to epidemics based on the severity and duration of the events.

Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-GARCH) models.

We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.