Knowledge that Transforms

In a model of oligopolistic competition in the banking sector, we analyse how the monetary policy rule chosen by the Central Bank can influence the incentive of banks to set high interest rates on loans over the business cycle. We exploit the basic model to investigate the potential impact of EMU implementation on collusion among banks. In particular, we consider the possible effects of the European Central Bank's policy criteria with regard to the cost of credit in national markets.

The microstructure of the Saudi Stock Market (SSM) under the new computerized trading system, ESIS, is described, and order and other generated data sets are used to examine the patterns in the order book, the dynamics of order flow, and the probability of executing limit orders. Although the SSM has a distinct structure, its intraday patterns are surprisingly similar to those found in other markets with different structures. We find that liquidity, as commonly measured by width and depth, is relatively low on the SSM. However, liquidity is exceptionally high when measured by immediacy. Limit orders that are priced reasonably, on average, have a short duration before being executed, and have a high probability of subsequent execution.

This paper examines how the affiliation of banking and commerce affects the firm’s investment efficiency and the bank’s risk exposure. The bank’s holding of a borrowing firm’s equity reduces the agency conflict between the firm and the bank, but increases the monitoring need of uninformed debtholders. Thus, the firm’s investment efficiency is maximized when the bank’s equity share is between zero and its debt share. The bank’s risk exposure can increase in two ways. With a large equity share, the bank has more incentives to allow the firm to undertake risky projects. The firm, when it has control over the bank, may force the bank to finance its risky projects.

This introduction places in context the papers on credit risk modelling contained in the special issue. We explain why credit risk modelling has become such a focus of interest for practitioners and financial supervisors. Even though, as we explain, the current modelling technologies have significant weaknesses, they offer the possibility of major changes in the ways banks are managed and regulated. The main impediment to greater use of these models, especially by regulators, is the difficulty involved in back-testing the risk measures they produce. We suggest some thoughts on how back-testing and other types of model assessment might be performed.

We study the rationality of learning and the biases in expectations in the Iowa Experimental Markets. Using novel tests developed in (Bossaerts, P., 1996. Martingale restrictions on equilibrium security prices under rational expectations and consistent beliefs. Caltech working paper; Bossaerts, P., 1997. The dynamics of equity prices in fallible markets. Caltech working paper), learning in the Iowa winner-take-all markets is found to be in accordance with the rules of conditional probability (Bayes' law). Hence, participants correctly update their beliefs using the available information. There is evidence, however, that beliefs do not satisfy the restrictions of rational expectations that they reflect the factual distribution of outcomes.

The discrete-time dynamic investment model, using only historical data in various asset-allocation settings, often produces significant abnormal returns. However, the model does not choose the diversified portfolios that theory suggests it should. Therefore, in this paper, we compare the investment policies and returns of the model with and without constraints on the mix of risky assets. The constraints lead to appreciably more diversification and less realized risk, but only at the cost of less realized return. Visual comparisons of compound return—standard deviation plots and statistical comparisons of Jensen’s alpha suggest that the reduction in return is not worth the reduction in risk. For more risk-averse investors, ex post utility and certainty equivalent returns suggest that it is. The results, however, illustrate the problems associated with using ex post utility to measure performance.

This paper demonstrates that the use of GARCH-type models for the calculation of minimum capital risk requirements (MCRRs) may lead to the production of inaccurate and therefore inefficient capital requirements. We show that this inaccuracy stems from the fact that GARCH models typically overstate the degree of persistence in return volatility. A simple modification to the model is found to improve the accuracy of MCRR estimates in both back- and out-of-sample tests. Given that internal risk management models are currently in widespread usage in some parts of the world (most notably the USA), and will soon be permitted for EC banks and investment firms, we believe that our paper should serve as a valuable caution to risk management practitioners who are using, or intend to use this popular class of models.

This paper examines the role of investment opportunities and free cash flow in explaining the source of the wealth effect of international joint ventures. We document that firms with promising investment opportunities have significantly positive response to announcements of international joint venture investments, whereas firms with poor investment opportunities have unfavorable response to such announcements. In contrast, we find that free cash flow does not explain the cross-sectional differences in abnormal returns associated with the announcements of international joint ventures. Thus, our results show support for the investment opportunities hypothesis but no support for the free cash flow hypothesis. These findings hold even after controlling for other potential explanatory variables.

Over the past decade, commercial banks have devoted many resources to developing internal models to better quantify their financial risks and assign economic capital. These efforts have been recognized and encouraged by bank regulators. Recently, banks have extended these efforts into the field of credit risk modeling. However, an important question for both banks and their regulators is evaluating the accuracy of a model’s forecasts of credit losses, especially given the small number of available forecasts due to their typically long planning horizons. Using a panel data approach, we propose evaluation methods for credit risk models based on cross-sectional simulation. Specifically, models are evaluated not only on their forecasts over time, but also on their forecasts at a given point in time for simulated credit portfolios. Once the forecasts corresponding to these portfolios are generated, they can be evaluated using various statistical methods.

The role of movements in real rates in explaining the relationship between long- and short-term interest rates is explored within a model of optimal government debt management. The government's incentives to resort in the future to inflation and ex post debt taxation in order to reduce the real value of its nominal liabilities have an impact on term premia and hence on the short–long spread. In particular, default risk and, consequently, long-term interest rates increase with the size of outstanding debt and the level of real rates; in the presence of short maturities, indexed debt and anti-inflationary governments. Optimal maturity either lengthens or shortens with inflation risk, depending on the time profile of government expenditure, while it always lengthens with default risk. However, when the stock of debt is extremely large the compensation for default risk required by the agents on long-term bonds may be so high that only short-term debt can be issued. The implications of this model are consistent with the observed behavior of risk premia in some highly indebted countries.