Risk and Decision Analysis

The agency problem between an investor and his mutual funds managers has long been studied in the economic literature. Because the very business of mutual funds managers is not only to manage money but also, and rather, to increase the money under management, one of the numerous agency problems is the implicit incentive induced by the relationship between inflows and performance. If the consequences of incentives, be they implicit or explicit – as for compensation schemes of individual asset managers – are well known in terms of risk-shifting when the incentives are linked to a benchmark, the very fact that the mutual fund market is a tournament does not seem to be modeled properly in the literature. In this paper, we propose a mean field games model to quantify the risk-shifting induced by a tournament-like competition between mutual funds.

This paper examines the response of a sample of Asian banks to the recognition of loan loss provision in the face of a gathering economic storm. Drawing on empirical data from 2006 through 2008, this paper focuses on the level of loan loss provisioning undertaken by the banks, with a view to generating insights into the effectiveness of the approach to loan impairment and provisioning prescribed by IAS 39 – Financial Instruments: Measurement and Recognition.

Given that the focus of impairment decision making under IAS 39 is historically oriented rather than future oriented, we argue this may result in the diminution in the decision usefulness of the content of bank financial statements in the face of imminent, though not yet manifested economic distress.

Despite evidence that substantial portions of the globe's financial and economic fabric was in a state of severe distress over this period, our analysis of the financial disclosures of the sample of Asian banks shows a picture at odds with this larger reality.

We argue that this response is shaped by the requirements of the newly introduced accounting standard and that a broadening of the legitimate sources of evidence upon which loan impairment recognition decisions may be based pursuant to IAS 39 should be a matter of priority.

Using both mean–variance portfolio optimization (MVPO) and stochastic dominance (SD) approaches, this paper investigates whether international diversification and home bias inertia are substitutes or complements for Americans. More specifically, we compare daily closing prices of 30 US stocks and the stock indices from American, Latin American, and Asian financial markets, including both emerging and major markets. Results from the MVPO show that a domestic diversification strategy performs better for any risk level up to 0.5%, whereas international diversification performs better for any risk level higher than 0.5%. Some results from the SD test support international diversification, some promote home bias, and still others conclude that there is no difference between investing domestically and internationally. However, our findings show that one could not find any single internationally diversified portfolio that dominates all domestically diversified portfolios and, similarly, one could not find any single domestically diversified portfolio that dominates all internationally diversified portfolios. At last, our SD findings also recommends that the US investors have a “home bias” if they prefer less risk and to be “internationally diversified” if they prefer higher risk.

Using recent, original approach, neutral and indifference pricing PDEs are derived for general multi-dimensional Markovian diffusive market models and, under certain conditions, for any utility of wealth. In the case of portfolios of contracts the pricing PDE system is proved for neutral, while conjectured and discussed for indifference pricing. Hedging formulas are given too. Some special cases are derived as well to demonstrate the consistency with the well known results.

One of the central issues in credit risk measurement and management is modeling and predicting correlated defaults. In this paper we introduce a novel model to investigate the relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults using the Probabilistic Boolean Network (PBN). The key idea of the PBN is to decompose a transition probability matrix describing correlated defaults of different sectors into several BN matrices which contain information about business cycles. An efficient estimation method based on an entropy approach is used to estimate the model parameters. Using real default data, we build a PBN for explaining the default structure and making reasonably good predictions of joint defaults in different sectors.