Alec G. Stephenson

Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields

We discuss an approach for the statistical modelling of extreme precipitation events in South-West Australia over space and time, using a latent spatiotemporal process where precipitation maxima follow a generalised extreme value distribution. Temporal features are captured by modelling trends on the location and scale parameters. Spatial features are captured using anisotropic Gaussian random fields. Site specific explanatory variables are also incorporated. We fit several models using Bayesian inferential methods to precipitation extremes recorded at 36 weather stations around the Western Australian state capital city of Perth over the period 1907–2009. Model choice is performed using the DIC criterion. The best fitting model shows significant non-stationarity over time, with extreme precipitation events becoming less frequent. Extreme precipitation events are stronger at coastal locations, with the intensity decreasing as we head to the higher and drier areas to the North-East.

Estimating Spatially Varying Severity Thresholds of a Forest Fire Danger Rating System Using Max-Stable Extreme-Event Modeling*

AbstractFire danger indices are used in many countries to estimate the potential fire danger and to issue warnings to local regions. The McArthur fire danger rating system is used in Australia. The McArthur forest fire danger index (FFDI) uses only meteorological elements. It combines information on wind speed, temperature, relative humidity, and recent rainfall to produce a weather index of fire potential. This index is converted into fire danger categories to serve as warnings to the local population and to estimate potential fire-suppression difficulty. FFDI values above the threshold of 75 are rated as extreme. The spatial behavior of large values of the FFDI is modeled to investigate whether a varying threshold across space may serve as a better guide for determining the onset of elevated fire danger. The authors modify and apply a statistical method that was recently developed for spatial extreme events, using a “max-stable” process to model FFDI data at approximately 17 000 data sites. The method t...

Measuring and forecasting the volatility of USD/CNY exchange rate with multi-fractal theory

Exchange rate fluctuations continue to intensify because of global economic integration. Research on the characteristics of exchange rate volatility is particularly urgent and important. In this paper, the fractal theory is introduced. The function box counting method and the qth-order moment structure partition function method are applied to test the multi-fractal features of USD/CNY exchange rate. On this basis, the multi-fractal spectrum analysis is carried out. It is found that USD/CNY exchange rate has multi-fractal characteristics and there is a strong connection between the standard deviation of the scale index and volatility of USD/CNY exchange rate. By adjusting the standard deviation of scaling exponents, we construct the multi-fractal volatility index and build a dynamic model for testing and forecasting the volatility of USD/CNY exchange rate based on fractal theory. The model

Vector generalized linear and additive extreme value models

\ln \bar{{S}}_\alpha -\hbox {ARMA} (1,1)

A software review for extreme value analysis

lnS¯α-ARMA(1,1) for measuring and forecasting volatility proposed in our paper is demonstrated to be a good fit to the exchange rate data, which provides sound methodological reference for exchange rate volatility measurement.

Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change

Noninterest income is what most Chinese banks are striving for in recent years because of the vigorous competition among commercial banks due to the increasingly open market and tough regulation from the central bank of China. But the problem is the real effect of noninterest income on profit and risks. A panel threshold model is used with balanced panel dataset of 16 listed Chinese commercial banks, for the period of 2007 to 2013, to investigate the relationship between noninterest income and performance. The findings show two main conclusions: (1) the existence of two thresholds shows that there is nonlinear relationship; (2) there is a general negative correlation between the noninterest income ratio and performance of commercial banks. Furthermore, when the noninterest income ratio is higher than the two thresholds, the negative correlation decreases. Implications of the paper are that the ratio should be controlled in a range or noninterest income will not positively affect the performance, and a high level of performance can be gained only by raising the ratio to a certain level.