Catálogo de publicaciones - libros

Basel II will dramatically change the allocation of regulatory equity capital to credit risk positions. Instead of an uniform 8 % capital charge regulatory equity capital will depend on the size of the credit risk, measured either by external or by internal rating systems. This will lead to a dramatic change in the bank-debtor relation. Credit spreads will widen and for high risk borrowers it may become difficult to get new loans. The major Basel II rules are surveyed and their consequences for bank lending are discussed.

There has been substantial public and regulatory attention of late to apparent exploitation of conflicts of interest involving financial services firms based on financial market imperfections and asymmetric information. This paper proposes a workable taxonomy of conflicts of interest in financial services firms, and links it to the nature and scope of activities conducted by such firms, including possible compounding of interest-conflicts in multifunctional client relationships. It lays out the conditions that either encourage or constrain exploitation of conflicts of interest, focusing in particular on the role of information asymmetries and market discipline, including the shareholder-impact of litigation and regulatory initiatives. External regulation and market discipline are viewed as both complements and substitutes — market discipline can leverage the impact of external regulatory sanctions, while improving its granularity though detailed management initiatives applied under threat of market discipline. At the same time, market discipline may help obviate the need for some types of external control of conflict of interest exploitation

Previous academic work has focused on why risk management at the corporate level is necessary and desirable from a value creation perspective rather than on how much or what sort of risk management is optimal for a particular firm/bank. Therefore, we develop in this chapter the foundations for a normative theory of risk management in banks. We first explain the need for a consistent framework for risk management at the corporate level in banks. We then move on to defining and examining RAROC (Risk-Adjusted Return on Capital), a capital budgeting rule currently widely used in the banking industry. We then introduce new approaches to capital budgeting and deduct implications from applying these new approaches in banks.

This paper addresses the capital requirements based on the RiskMetrics™ framework and the BIS standard model. A case study is developed which shows that the capital requirements can be reduced by applying the more accurate RiskMetrics™ framework. Furthermore it gives an overview of the capital requirement rules for credit risk and operational risk in the Basel II Accord.

This article surveys several applicational as well as theoretical aspects of Value at Risk as a measure of risk. First, we compare different calculation methods with respect to accuracy, implementational issues as well as suitability for resource allocation and optimization. We contribute to capital allocation based on Value at Risk and provide an optimization model. Afterwards, we concentrate on shortcomings of Value at Risk as a measure of risk from a theoretical point of view. The focus is on the relation to decision theory and to coherent measures of risk. Alternatives to Value at Risk such as the lower partial moment one or the tail conditional expectation are included. We give some reasons to prefer the latter as a measure of risk.

The standard approach to the risk analysis of fixed income portfolios involves a mapping of exposures into representative duration buckets. This approach does not provide a transparent description of the portfolio risk in the case of leveraged portfolios, particularly in the case of portfolios whose primary intent is to trade convexity. In this paper, an alternative approach, based upon Level, Slope and Curvature yield curve factors, is described. The alternative approach offers a linear model of non-linear trading strategies.

Our analysis focuses on the risk budgeting process for banks using value at risk limits. In this context, we investigate three major practical problems: a) differences in time horizons between the bank’s total risk budget and the trading divisions’ activities; b) adjustment for accumulated profit and losses to risk budgets, and c) incorporation of correlations between assets into the risk budgeting process. To analyze these practical problems, we use Monte Carlo simulation. Thereby, it can be shown that differences in time horizons among risk budgets and trading units can be adjusted by the square root of time rule. Three types of limits are proposed for the adjustment of accumulated profit and losses: the fixed, stop loss and dynamic limits. While the two latter restrict the maximum loss to the ex ante specified limit and show a symmetric profit and loss distribution, the dynamic limit’s distribution is skewed to the right. We further illustrate that the average usage of total risk capital is only 31.45 % for a trading division with thirty independently deciding traders. This shortfall is due to diversification effects. This setting is compared with a benchmark model in which total risk capital is always used at the full capacity of 100 %. The comparison shows that the average profit in the former model is only 33.13 % of the generated profit in the benchmark model. The results may have interesting organizational implications on the banking sector.

We study the implications of the value at risk (VaR) concept for the optimum amount of equity capital of a banking firm in the presence of credit risk. As a risk management tool VaR allows to control for the probability of bankruptcy. It is shown that the required amount of equity capital depends upon managerial and market factors, and that equity and asset/liability management has to be addressed simultaneously.

This paper explores different specifications of conditional return expectations. We compare the most common specification, linear least squares, with nonparametric techniques. Our results indicate that nonparametric regressions capture some nonlinearities in financial data. In-sample forecasts of international stock market returns are improved with nonparametric techniques. However, there is very little out-of-sample prediction power for both linear and nonparametric specifications of conditional expectations. If an asset manager relies on a simple instrumental variable regression framework to forecast stock returns, our results suggest that linear conditional return expectations are a reasonable approximation.

In this chapter, we conclude the study of stationary solutions and describe several suggestions obtained by this for the dynamics of (3.1), where Ω x2282; Rn is a bounded domain with smooth boundary Ω, a > 0 is a constant, and ν is the outer unit vector on ∂Ω. This system was proposed by Nagai [106] in the context of chemotaxis in mathematical biology. Here, u = u(x, t) and v = v(x, t) stand for the density of cellular slime molds and the concentration of chemical substances secreted by themselves, respectively, at the position x Ω and the time t > 0.