Measuring the Interdependence of Banks in Hong Kong

This paper assesses systemic linkages among banks in Hong Kong using the risk measure "CoVaR" derived from quantile regression. The CoVaR measure captures the co-movements of banks¡¯ default risk by taking into account their nonlinear relationship when the banks are in distress. Based on equity price information, our estimation results show that the default risks of the banks were interdependent during the recent crisis. Although local banks are generally smaller, their systemic importance is found to be similar to their international and Mainland counterparts, which may be due to a higher degree of commonality in the risk profile of local banks. Regarding the impact of external shocks on the banks, international banks are more likely to be affected by the equity price fall in the US market, while local banks are relatively more responsive to funding liquidity risk.Value-at-Risk, Systemic Risk, Risk Spillovers, Quantile Regression

The effects of non-universal extra dimensions on the radiative lepton flavor decays \mu\to e\gamma and \tau\to \mu\gamma in the two Higgs doublet model

We study the effect of non-universal extra dimensions on the branching ratios of the lepton flavor violating processes \mu\to e\gamma and \tau\to \mu\gamma in the general two Higgs doublet model. We observe that these effects are small for a single extra dimension, however, in the case of two extra dimensions there is a considerable enhancement in the additional contributions.Comment: 16 Pages, 9 Figure

African-American crack abusers and drug treatment initiation: barriers and effects of a pretreatment intervention

BACKGROUND: Individual and sociocultural factors may pose significant barriers for drug abusers seeking treatment, particularly for African-American crack cocaine abusers. However, there is evidence that pretreatment interventions may reduce treatment initiation barriers. This study examined the effects of a pretreatment intervention designed to enhance treatment motivation, decrease crack use, and prepare crack abusers for treatment entry. METHODS: Using street outreach, 443 African-American crack users were recruited in North Carolina and randomly assigned to either the pretreatment intervention or control group. RESULTS: At 3-month follow-up, both groups significantly reduced their crack use but the intervention group participants were more likely to have initiated treatment. CONCLUSION: The intervention helped motivate change but structural barriers to treatment remained keeping actual admissions low. Policy makers may be interested in these pretreatment sites as an alternative to treatment for short term outcomes

What Drives Hong Kong Dollar Swap Spreads: Credit or Liquidity?

This paper investigates the determinants of variations in the yield spreads (swap spreads) between Hong Kong dollar interest rate swaps and Exchange Fund paper for a period from July 2002 to April 2008. A vector error-correction model is used to analyse the impact of various shocks on swap spreads. The issue is whether "liquidity" or "credit" (or both) is the main determinant of swap spread dynamics. The results show that the dynamics are influenced significantly by "credit" between July 2002 and September 2007. However, "liquidity" between the Exchange Fund long-term notes and short-term bills is the major determinant of swap spreads between September 2007 and April 2008. The substantial demand of the Exchange Fund short-term bills, that reflected the strong preference of market participants for holding short-term instruments for liquidity purposes probably due to the sub-prime crisis in the US, is the driving force of the rise in swap spreads in the last quarter of 2007.Hong Kong dollar interest rates, swap spreads, vector error-correction model, sub-prime crisis

Forecasting a Large Dimensional Covariance Matrix of a Portfolio of Different Asset Classes

In portfolio and risk management, estimating and forecasting the volatilities and correlations of asset returns plays an important role. Recently, interest in the estimation of the covariance matrix of large dimensional portfolios has increased. Using a portfolio of 63 assets covering stocks, bonds and currencies, this paper aims to examine and compare the predictive power of different popular methods adopted by i) market practitioners (such as the sample covariance, the 250-day moving average, and the exponentially weighted moving average); ii) some sophisticated estimators recently developed in the academic literature (such as the orthogonal GARCH model and the Dynamic Conditional Correlation model); and iii) their combinations. Based on five different criteria, we show that a combined forecast of the 250-day moving average, the exponentially weighted moving average and the orthogonal GARCH model consistently outperforms the other methods in predicting the covariance matrix for both one-quarter and one-year ahead horizons.Volatility forecasting; Risk management; Portfolio management; Model evaluation

Comparing Forecast Performance of Exchange Rate Models

Exchange-rate movement is regularly monitored by central banks for macroeconomic-analysis and market-surveillance purposes. Notwithstanding the pioneering study of Meese and Rogoff (1983), which shows the superiority of the random-walk model in out-of-sample exchange-rate forecast, there is some evidence that exchange-rate movement may be predictable at longer time horizons. This study compares the forecast performance of the Purchasing Power Parity model, Uncovered Interest Rate Parity model, Sticky Price Monetary model, the model based on the Bayesian Model Averaging technique, and a combined forecast of all the above models with benchmarks given by the random-walk model and the historical average return. Empirical results suggest that the combined forecast outperforms the benchmarks and generally yields better results than relying on a single model.Bayesian Analysis, Model Evaluation and Selection, Forecasting and Other Model Application