Macro-financial linkages and bank behaviour: evidence from the second-round effects of the global financial crisis on East Asia

This paper studies the link between macro-financial variability and bank behaviour, which justifies the second-round effects of the global financial crisis on East Asia. Following Gallego et al. (The impact of the global economic and financial crisis on Central Eastern and South Eastern Europe (CESEE) and Latin America, 2010), the second round effects are defined as the adverse feedback loop from the slumps in economic activities and sharp financial market deterioration, which may influence the financial performance of bank, inter alia via deteriorating credit quality, declining profitability and increasing problems in retaining necessary capitalization. Differentiating itself from other research, this study stresses adjustments in four dimensions of bank performance and behaviour: asset quality, profitability, capital adequacy, and lending behaviour, assuming that any change in a bank-specific characteristic is induced by endogenous adjustments of the others. The empirical results based on partial adjustment models and two-step system GMM estimation show that bank’s adjustment behaviour is subject to the variation in the macro-financial environment and the stress condition in the global financial market. There is no convincing evidence to support the effectiveness of policy rate cut to boots bank lending and to avoid a financial accelerator effect

The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting

In this paper, we assess the informational content of daily range, realized variance, realized bipower variation, two time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out-of-sample Value-at-Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi-period VaR estimates. The VaR forecasts are evaluated in terms of statistical and regulatory accuracy as well as capital efficiency. Our empirical findings, based on the S&P 500 stock index, indicate that almost all realized and implied volatility measures can produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being especially accurate in monthly VaR forecasts. The daily range produces inferior forecasting results in terms of regulatory accuracy and Basel II compliance. However, robust realized volatility measures such as the adjusted realized range and the realized bipower variation, which are immune against microstructure noise bias and price jumps respectively, generate superior VaR estimates in terms of capital efficiency, as they minimize the opportunity cost of capital and the Basel II regulatory capital. Our results highlight the importance of robust high frequency intra-daily data based volatility estimators in a multi-step VaR forecasting context as they balance between statistical or regulatory accuracy and capital efficiency

Credit Information Sharing and Loan Default in Developing Countries: The Moderating Effect of Banking Market Concentration and National Governance Quality

Departing from the existing literature, which associates credit information sharing with improved access to credit in advanced economies, we examine whether credit information sharing can also reduce loan default rate for banks domiciled in developing countries. Using a large dataset covering 879 unique banks from 87 developing countries from every continent, over a nine-year period (i.e., over 6,300 observations), we uncover three new findings. First, we find that credit information sharing reduces loan default rate. Second, we show that the relationship between credit information sharing and loan default rate is conditional on banking market concentration. Third, our findings suggest that governance quality at the country level does not have a strong moderating role on the effect of credit information sharing on loan default rate

A Quick Tool to Forecast VaR Using Implied and Realized Volatilities

We propose here a naive model to forecast ex­ante Value­at­Risk (VaR) using a shrinkage estimator between realized volatility estimated on past return time series, and implied volatility extracted from option pricing data. Implied volatility is often indicated as the operators expectation about future risk, while the historical volatility straightforwardly represents the realized risk prior to the estimation point, which by definition is backward looking. In a nutshell, our prediction strategy for VaR uses information both on the expected future risk and on the past estimated risk. We examine our model, called Shrinked Volatility VaR, both in the univariate and in the multivariate cases, empirically comparing its forecasting power with that of two benchmark VaR estimation models based on the Historical Filtered Bootstrap and on the RiskMetrics approaches. The performance of all VaR models analyzed is evaluated using both statistical accuracy tests and efficiency evaluation tests, according to the Basel II and ESMA regulatory frameworks, on several major markets around the world over an out­of­ sample period that covers different financial crises. Our results confirm the efficacy of the implied volatility indexes as inputs for a VaR model, but combined together with realized volatilities. Furthermore, due to its ease of implementation, our prediction strategy to forecast VaR could be used as a tool for portfolio managers to quickly monitor investment decisions before employing more sophisticated risk management systems