An examination of bank risk measures and their relationship to systemic risk measurement : a dissertation presented in partial fulfilment of the requirements for the degree of Doctoral of Philosophy in Finance at Massey University, Manawatu (Turitea), New Zealand

This research explores ways of measuring bank risk, both individual bank risk and systemic risk, with the main focus on z-score. Z-score is a popular indicator of individual bank risk-taking. Despite its popularity among academics, there is a lack of consensus on a standard way to construct a time-varying z-score measure. Meanwhile, in the post-GFC period, increasing attention has been given to macro-prudential policy and its role in mitigating systemic risk. This research discusses major challenges in existing approaches to the construction of time-varying z-score measure. It empirically compares these approaches using quarterly data of New Zealand banks. Both conceptual discussions and empirical analyses support the use of a rolling window in the computation of time-varying z-score, which is consistent with changing bank risk profiles through time. This research is also the first study to propose a risk-weighted z-score measure. This research further proposes a new systemic risk measure based on z-score, which is developed on the concept of Leave-One-Out (LOO) approach. The systemic risk contribution of an individual bank can be captured by the variation of risk-taking of a banking system when excluding the particular bank. The LOO z-score measure can be computed using accounting information only, and is therefore applicable to both listed and unlisted banks. Empirical analysis on the LOO z-score measure in assessing banks’ systemic risk contribution is first applied to the New Zealand and Australian markets, and then extended to an international sample including 17 countries. The LOO z-score measure is proved to be useful for assessing banks’ systemic risk contribution, with a positive rank correlation with Marginal Expected Shortfall (MES) and Delta Conditional Value-at-Risk (ΔCoVaR). The LOO z-score measure provides a new approach to assess systemic risk contribution using accounting data, which can be used as a complement to market-based approaches. This measure is especially useful for systemic risk analyses of banks with limited or even no share market data at all, which is the key advantage. The ability to include both listed and unlisted banks in the evaluation of systemic risk is fundamental in macro-prudential policy frameworks

Gamma-Ray Bursts in 1.8<z<5.61.8 < z < 5.6 Suggest that the Time Variation of the Dark Energy is Small

We calibrated the peak energy-peak luminosity relation of GRBs (so called Yonetoku relation) using 33 events with the redshift $z < 1.62$ without assuming any cosmological models. The luminosity distances to GRBs are estimated from those of large amount of Type Ia supernovae with $z<1.755$. This calibrated Yonetoku relation can be used as a new cosmic distance ladder toward higher redshifts. We determined the luminosity distances of 30 GRBs in $1.8 < z < 5.6$ using the calibrated relation and plotted the likelihood contour in $(\Omega_m,\Omega_\Lambda)$ plane. We obtained $(\Omega_m, \Omega_{\Lambda})= (0.37^{+0.14}_{-0.11}, 0.63^{+0.11}_{-0.14})$ for a flat universe. Since our method is free from the circularity problem, we can say that our universe in $1.8 < z < 5.6$ is compatible with the so called concordance cosmological model derived for $z < 1.8$. This suggests that the time variation of the dark energy is small or zero up to $z\sim 6$.Comment: 4 pages, 4 figures, accepted to MNRA

Production of regional 1 km x 1 km water vapor fields through the integration of GPS and MODIS data

&lt;p&gt;Atmospheric water vapor is a crucial element in weather, climate and hydrology. With the recent advance in Global Positioning System (GPS) Meteorology, ground-based GPS has become an operational tool that can measure precipitable water vapor (PWV) with high accuracy (1~1.5mm) during all-weathers, and with high temporal resolution (e.g. 5 minutes) at low cost. But the spatial coverage of GPS receivers is limited, and restricts its applications. At present, two NASA Moderate Resolution Imaging Spectroradiometer (MODIS) can provide global coverage 2D water vapor field with a spatial resolution of 1 km × 1 km (at nadir) every 2 days, and at many latitudes can provide water vapor fields every 90 minutes, 4 times a day. The disadvantages of MODIS water vapor products are: 1). A systematic uncertainty of 5-10% is expected [Gao et al., 2003; Li et al., 2003]; 2). Since the MODIS water vapor retrieval relies on observations of water vapor attenuation of near Infrared (IR) solar radiation reflected by surfaces and clouds, it is sensitive to the presence of clouds. The frequency and the percentage of cloud free conditions at mid-latitudes is only 15-30% on average [Li et al., 2004]. Therefore, in order to extract a water vapor field above the Earth’s surface, an attempt needs to be made to fill in the cloudy pixels.&lt;/p&gt; &lt;p&gt;In this paper, an inter-comparison between MODIS (collection 4) and GPS PWV products was performed in the region of the Southern California Integrated GPS Network (SCIGN). It is shown that MODIS appeared to overestimate PWV against GPS with a scale factor of 1.05 and a zero-offset of –0.7 mm. Taking into account the small standard deviation of the linear fit model, a GPS-derived correction linear fit model was proposed to calibrate MODIS PWV products, and a better agreement was achieved. In order to produce regional 1 km × 1 km water vapor fields, an integration approach was proposed: Firstly, MODIS near IR water vapor was calibrated using GPS data; secondly, an improved inverse distance weighted interpolation method (IIDW) was applied to fill in the cloudy pixels; thirdly, the densified water vapor field was validated using GPS data. It is shown that the integration approach was promising. After correction, MODIS and GPS PWV agreed to within 1.6 mm in terms of standard deviations using appropriate extent and power parameters of IIDW, and the coverage of water vapor fields increased by up to 21.6%. In addition, for the first time, spatial structure functions were derived from MODIS near IR water vapor, and large water vapor variations were observed from time to time.&lt;/p&gt