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This study employs a multivariate GARCH model to investigate the relative sensitivities of the first and the second moment of bank stock return distribution to the short‐term and long‐term interest rates and their respective volatilities. Three portfolios are formed representing the money center banks, large banks, and small banks, respectively. Estimation and testing of hypotheses are carried out for each of the three portfolios separately. The sample includes daily data over the 1988‐2000 period. Several hypotheses are tested within the multivariate GARCH specification. These include the hypotheses of: (i) insensitivity of bank stock return to the changes in the short‐term and long‐term interest rates, (ii) insensitivity of bank stock returns to the changes in the volatilities of short‐term and long‐term interest rates, and (iii) insensitivity of bank stock return volatility to the changes in the short‐term and long‐term interest rate volatilities. The findings indicate that short‐term and long‐term interest rates and their volatilities do exert significant and differential impacts on the return generation process of the three bank portfolios. The magnitudes and the direction of the effect are model‐specific namely that they depend on whether the short‐term or the long‐term interest rate level is included in the mean return equation. These findings have implications on bank hedging strategies against the interest rate risk, regulatory decisions concerning risk‐based capital requirement, and investor’s choice of a portfolio mix.

This study attempts to determine whether the level and volatility of interest rates affect the equity returns of commercial banks. Short‐term, intermediate‐term, and long‐term interest rates are used. Volatility is defined as the conditional variance of respective interest rates and is generated by using the ARCH estimation procedure. Two sets of models are estimated. The basic models attempt to determine the effect of contemporaneous and lagged interest rate volatility on bank equity returns, while the extended models incorporate additional contemporaneous macroeconomic variables. Contemporaneous interest rate volatility has little explanatory power, while lagged volatilities do possess some explanatory power, with the lag length varying depending on the interest rate series used and the time period examined. The results from the extended model suggest that the long‐term interest rate affects bank equity returns more adversely than the short‐term or the intermediate‐term interest rates. The findings establish the relevance of incorporating macroeconomic variables and their volatilities in models determining bank equity returns.

The purpose of this paper is to consider the problem of using “black‐box” methods to forecast catastrophe events, and illustrate the value of independent peer review.

The problem with black‐box catastrophe forecasts is the absence of both extensive validation data and impartial peer review. These issues may be addressed by comparing black‐box forecasts with a set of naïve alternative forecasts provided by an independent party. To illustrate this approach, the historical hurricane forecasts of Dr William M. Gray, professor at Colorado State University, are considered and a simple ARIMA analysis is offered as a naïve alternative.

The analysis shows that Dr Gray's complex forecasting methodology does in fact provide reasonable forecasts, and may indeed offer value beyond a naïve alternative model.

The editorial identifies a major problem in catastrophe forecasting, and suggests one way to address this problem.

The purpose of this paper is to show that subordinated debt regulatory proposals assume that transactions in the secondary market of subordinated debt can attenuate moral hazard on the part of management if secondary market prices are informative signals of the risk of the institution. Owing to the proprietary nature of dealer prices and the liquidity of secondary transactions, the practical value of information provided by subordinated debt issues in isolation is questionable.

A multivariate dynamic risk signal is proposed that combines fluctuations in equity prices, subordinated debt and senior debt yields. The signal is constructed as a coincident indicator that is based in a time series model of yield fluctuations and equity returns. The extracted signal monitors idiosyncratic risk of the intermediary because yields and equity returns are filtered from market conditions. It is also predictable because it is possible to construct a leading indicator based almost entirely on spreads to Treasury.

The signal for the Bank of America and Banker's Trust is implemented. For Bank of America, the signal points mainly to two events of uprising risk: January 2000 when the bank disclosed large losses in its bond and interest‐rate swaps portfolios; and November 2000 when it wrote off $1.1 billion for bad loans. For Banker's Trust, the signal points to October/November 1995 after the filing of federal racketeering charges against Banker's Trust; and October 1998 when the bank suffered substantial losses from its investments in emerging markets.

The signal is a complementary instrument for regulators and investors to monitor and assess in real time the risk profile of the financial institution.

The purpose of this research is to develop and test a mathematical method of deriving zero yield curve from market prices of government bonds.

The method is based on a forward curve approximated by a linear (or piecewise constant) spline and should be applicable even for markets with low liquidity. The best fitting curve is derived by minimizing the penalty function. The penalty is defined as a sum of squared price discrepancies (theoretical curve based price minus market closing price) weighted by trade volume and an additional penalty for non‐smoothness of the yield curve.

This method is applied to both nominal and CPI linked bonds traded in Israel (some segments of these markets have low liquidity). The resulting two yield curves are used for derivation of market expected inflation rate.

The main problems are low liquidity of some bonds and imperfect linkage to inflation in the CPI linked market.

A stable numerical procedure applicable even in markets with low liquidity.

Usage of forward curves as the state space for the minimization problem leads to a stable solution that fits the data very well and can be used for calculating forward rates.

Bank profit warnings represent a milder form of negative news than a bank failure. Yet, they may contain signals about a bank or its rivals because the information is transmitted when the bank believes that the market is overly optimistic about its future earnings. Thus, the profit warning serves as a means by which insiders of the bank can reduce the asymmetric information between the bank’s insiders and its investors. We find that banks experience negative valuation effects in response to their profit warnings. The banks’ profit warnings result in significant negative valuation effects for its corresponding rival banks, which implies that the warning carries valuable information about banking industry conditions. However, the effects on rivals are attenuated since the passage of Regulation Fair Disclosure (RFD). This implies that investors may be relying on more transparent sources of information about individual banks rather than relying on one bank’s warning as a signal about other banks. Furthermore, bank regulations may allow for more transparent communication by banks than that of nonbank firms.

The purpose of this paper is to investigate the effect of revenue diversification, non-interest income and asset diversification on the performance and stability of the Gulf Cooperation Council (GCC) conventional and Islamic banking systems.

The authors implement a panel of 69 conventional and Islamic banks listed in six GCC markets over the period of 2003–2015, using the System Generalized Method of Moments methodology.

Non-interest income diversification has a negative impact on GCC banks’ performance, while asset-based diversification affects banks performance positively. However, Investors tend to penalize the value of the banks’ assets, which are highly diversified. Government intervention, lack of competition, legal protection and high control of Central banks on GCC banks’ have positive impact on performance. Contrary to the results on conventional banks, asset diversification adds value to Islamic banks. Overall, both banks’ revenue and non-interest diversification have negative impact on GCC banks’ stability, while asset diversification improves Islamic banks’ stability.

The analysis is limited to a sample of banks, which are listed in the GCC stock exchanges. The lack of data on private and foreign banks operating in the region made the analysis and, consequently, the results specific to shareholding companies. Also, the authors’ measures of bank stability might not be appropriate to use for Islamic banks, given their banking models implemented.

Research results provide important implications for regulators, bank managers and policy makers, as to the expected ways to support economic diversification through bank diversification strategies.

Unlike related studies, the authors’ sample of homogeneous banks has a market structure that is different from the samples in the literature covering either developed countries or heterogeneous samples from both developed and developing countries. Furthermore, using an efficient econometric methodology, the authors deal with two types of banks: conventional banks and Islamic banks. The research determines which type of bank is more able to benefit from different types of diversification. Unlike previous research, this research explores the sensitivity of the results both to the regulatory environment of the GCC market and to general market conditions.

Hong Kong and Singapore are economically similar and rival international financial centers. Banks in both Hong Kong and Singapore operate in very similar environments: internationally oriented with protected domestic banking market and firm regulators. With liberalization under the Financial Services Accord of the World Trade Organization (WTO), comes more competition and the growing importance for banks to ensure that they are X‐efficient so as to compete successfully or risk being marginalized. This paper uses data envelopment analysis (DEA) to assess X‐efficiency of banks in Hong Kong and Singapore via a two‐stage (combining both the intermediation and production stages) banking model. Changes in X‐efficiency over time are computed to determine if policy initiatives have facilitated improvements in efficiency. Our results on X‐efficiency of banks demarcated by size and ownership provide valuable insights into the issues of scale economies and the impact of family ownership on X‐efficiency.