Risk allocation under liquidity constraints

Abstract Risk allocation games are cooperative games that are used to attribute the risk of a financial entity to its divisions. In this paper, we extend the literature on risk allocation games by incorporating liquidity considerations. A liquidity policy specifies state-dependent liquidity requirements that a portfolio should obey. To comply with the liquidity policy, a financial entity may have to liquidate part of its assets, which is costly. The definition of a risk allocation game under liquidity constraints is not straightforward, since the presence of a liquidity policy leads to externalities. We argue that the standard worst case approach should not be used here and present an alternative definition. We show that the resulting class of transferable utility games coincides with the class of totally balanced games. It follows from our results that also when taking liquidity considerations into account there is always a stable way to allocate risk

Recovering Risky Technologies Using the Almost Ideal Demand System: An Application to U.S. Banking

We argue for a shift in the focus of modeling production from the traditional assumptions of profit maximization and cost minimization to a more general assumption of managerial utility maximization that can incorporate risk incentives into the analysis of production and recover value-maximizing technologies. We show how this shift can be implemented using the Almost Ideal Demand System. In addition, we suggest a more general way of measuring efficiency that can incorporate a concern for the market value of firms' assets and equity and identify value-maximizing firms. This shift in focus bridges the gap between the risk-incentives literature in banking that ignores the microeconomics of production and the production literature that ignores the relationship between production decisions and risk.

Recovering risky technologies using the almost ideal demand system: an application to U.S. banking

The authors argue for a shift in the focus of modeling production from the traditional assumptions of profit maximization and cost minimization to a more general assumption of managerial utility maximization that can incorporate risk incentives into the analysis of production and recover value-maximizing technologies. The authors show how this shift can be implemented using the Almost Ideal Demand System. In addition, the authors suggest a more general way of measuring efficiency that can incorporate a concern for the market value of firms' assets and equity and identify value-maximizing firms. This shift in focus bridges the gap between the risk-incentives literature in banking that ignores the microeconomics of production and the production literature that ignores the relationship between production decisions and risk.Banks and banking - Costs ; Banks and banking

Hedging bond portfolios versus infinitely many ranked factors of risk

The paper considers bond portfolios affected by both interest-rate- and default-risk. In order to guarantee a correct performance of our analysis we will hedge against an infinite number of factors. Hence we do not have to impose and do not depend on any assumption concerning the dynamic behavior of the term structure of interest rates. On the other hand, since a complete hedging is not feasible unless some ideal situations hold, we rank the factors according to the empirical evidence. Thus, we make the most important risks vanish and we minimize the effect of those kinds of risk less usual in practice

Hedging bond portfolios versus infinitely many ranked factors of risk

The paper considers bond portfolios affected by both interest-rate- and default-risk. In order to guarantee a correct performance of our analysis we will hedge against an infinite number of factors. Hence we do not have to impose and do not depend on any assumption concerning the dynamic behavior of the term structure of interest rates. On the other hand, since a complete hedging is not feasible unless some ideal situations hold, we rank the factors according to the empirical evidence. Thus, we make the most important risks vanish and we minimize the effect of those kinds of risk less usual in practice.

Accurate value-at-risk forecast with the (good) old normal-GARCH model

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L. Klassifizierung: C22, C53, C63, G12Die Normalverteilung ist, entgegen ihrer hohen Verbreitung in der empirischen Finanzanalyse, im allgemeinen nicht dazu geeignet, die Renditen von Finanzmarkt-Zeitreihen adäquat zu beschreiben. Ein viel beobachtetes PhÄanomen ist insbesondere die über die Zeit variierende Volatilität der Renditen, die eine bedingte Modellierung der Renditen notwendig erscheinen läßt. Der wohl am weitesten verbreitete Ansatz um solche Volatilitätsschwankungen zu modellieren ist das GARCH-Modell. Doch auch bei Berücksichtigung der VolatilitÄatschwankungen, d.h. bei bedingter Modellierung der Renditen mit Hilfe eines GARCH-Modells, ist die Normalverteilung im allgemeinen nicht dazu geeignet, die Verteilung der GARCH-gefilterten Renditen ausreichend genau zu beschreiben. Insbesondere Value-at-Risk (VaR) Prognosen sind mit dem normal-GARCH Modell im allgemeinen verzerrt, da die Normalverteilung die Enden der Rendite-Verteilung nur unzureichend beschreibt. Mögliche Auswege scheinen die Erweiterung und Modifikation der GARCH Dynamik, sowie die Verwendung anderer Verteilungen. Dies führt jedoch im allgemeinen dazu, daß diese Modelle sowohl theoretisch, als auch praktisch schwerer zu beherrschen sind. In der vorliegenden Studie entwickeln wir eine auf dem Bootstrap basierende Methode mit einem Verzerrungs-Korrektur Schritt, um die VaR Prognoseeigenschaften des normal-GARCH Modells zu verbessern. Im Vergleich zur Verwendung von komplexeren GARCH Spezifikationen und/oder Verteilungsannahmen ist diese neue Methode schnell, einfach zu implementieren, numerisch zuverlässig und (abgesehen von einer zu wählenden Fensterlänge L für den Schritt zur Korrektur der VaR-Verzerrung) vollst ndig Daten getrieben. Die vorgeschlagene Methode wird in langen out-of-sample Prognosezeiträumen auf ihre VaR Prognosefähigkeiten geprüft. Sowohl für verschiedene Finanzmarkt-Reihen, als auch für simulierte Daten, erweist sich die neue Methode als sehr gut geeignet, die VaR Prognosen der normal-GARCH Modells entscheidend zu verbessern und liefert auch im Vergleich zu komplexeren Modellen sehr gute Ergebnisse

Accurate Value-at-Risk Forecast with the (good old) Normal-GARCH Model

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L.Bootstrap, GARCH, Value-at-Risk