Global Portfolio Optiomization Revisted: A Least Discrimination Alternative to Black-Litterman

Global portfolio optimization models rank among the proudest achievements of modern finance theory, but practitioners are still struggling to put them to work. In 1992, Black and Litterman put the problem down to difficulties portfolio managers have in extrapolating views about some expected asset returns into full probabilistic forecasts about all asset returns and proposed a method to alleviate this problem. We propose a more general method based on a least discrimination (LD) principle. It produces a probabilistic forecast that remains true to personal views but is otherwise as close as possible to the forecast implied by a reference portfolio. The LD method produces optimal portfolios for a variety of views, including views on volatility and correlation, in which case optimal portfolios include option-like pay-offs. It also justifies a simple linear interpolation between market and personal forecasts, should a compromise be reached.Global portfolio optimization, black-litterman model, least discrimination, utility theory, mean-variance analysis, relative entropy, generalized relative entropy, non-linear pay-offs

A Constructive Review of Basel's Proposals on Operational Risk

Risk and loss are common words that need to be clearly defined when embarking on the task of assessing operational risks. Financial institutions may rush into implementing the methodologies proposed by Basel in the hope of achieving better risk management – or simply to satisfy a regulatory request – but without giving enough thoughts to this enterprise. We show that the methodologies proposed by Basel to assess risks and calculate capital requirements are indeed poorly defined and, as far as they can be understood, misconceived. When restricting our attention to operational risks we find that their impact in the vast majority of cases is negligible compared to other risks, be they credit, market or general business risks. A few truly exceptional operational risks may, of course, lead to catastrophic consequences, but then the answer is not in an extra capital buffer that would have to be enormous to be of any use. An attempt to aggregate purely operational risks, as proposed by Basel in the so-called Advanced Measurement Approaches, is as futile as it is difficult. What matters in risk management is balancing all risks, whatever they are, against costs and revenues. And risks do not add up; it is the interaction between operational risks and other risks and the risk/reward trade-off that is of interest. Basel recognises this broader aspect of operational risk management in its guidance notes for the development of an operational risk management framework and the supervision of risk management. Recent redrafting of these notes suggest a change of emphasis from loss data collection towards more forward-looking risk assessment and comprehensive risk management.Financial Markets, Policy and Regulation, Operational Risk, Basel Accord, Capital Models, Risk Aggregation

Operational Risk Management

We view risk management as an integral part of good management. Risk management should take a balanced view of decision problems encompassing all significant risks and rewards. Operational risks are only one type of risks and therefore are only one piece in the jigsaw puzzle that only makes sense when all pieces are assembled. All risk analyses are based on the same general principles – generation of alternatives, quantification of uncertainties and preferences, modeling of consequences – but factors deserving the most attention vary from problem to problem. We distinguish three broad types of operational risks according to the frequencies of loss events: nominal, ordinary and exceptional. Depending on the type, uncertainties are negligible, similar or very large compared to expected losses. Nominal risks are the province of Total Quality Management, a well-developed discipline, but perhaps better known in manufacturing than in financial services. The analysis of ordinary and exceptional risks is illustrated by case studies from which we draw general lessons. With ordinary risks, it is crucial to understand the interaction among risks and with costs and rewards; risks do not add up, indeed operational risks may sometime reduce other uncertainties. With exceptional risks, we show the importance of quantifying the risk attitude of a financial institution in order to arrive at rational decisions such as mitigation or transfer of risks.Risk management, poerational risk, risk attitufe, utility

Application-Based Financial Risk Aggregation methods

Financial risks are usually analysed by type and by activity using different assumptions and methodologies as may seem appropriate in each case. This approach makes it very difficult to ascertain the degree of diversification between various activities and to obtain a proper estimate of global risk. We show that different risk aggregation methodologies should be used depending on the purpose of the exercise. In particular, if it is to promote an efficient allocation of resources, a short term, normal circumstances view should be adopted, but if it is to ensure a high degree of financial soundness over the long term, then extreme circumstances and contingency plans should be explored. We propose a simple linear risk factor model in the first case but suggest that a full business model is required for the second. Finally, financial regulators raise an intermediate question that is almost impossible to answer, namely, what is the minimum level of capital consistent with a probability of default of the firm of 0.1% over one year, that is consistent with a single ‘A’ rating. We suggest that an extension of our normal risk factor model to estimate ‘tail’ effects could give a better approximation than the current regulatory rules.

The Relative Merits of Investable Hedge Fund Indices and of Funds of Hedge Funds in Optimal Passive Portfolios

Can the new investable hedge fund indices (IHF) enhance the performance of optimal passive portfolios made of equities and bonds? How do they compare to funds of hedge funds (FoHF) as well as to other alternative investments such as commodities and volatility? The conclusions depend crucially on forecasts of future expected excess returns for all assets as well as a careful conditioning of the data to reflect trading costs and remove unrealistic serial correlations. A naïve forecast based on recent historical performance leads to no allocations to either IHF or FoHF, a result explained by the performance of equities and commodities and limited diversification effects from hedge funds. Yet a forecast based on market equilibrium returns for all main asset classes but hedge funds, which are kept at their historical level, leads to the opposite result with optimal portfolios almost exclusively invested in hedge funds. Both conclusions are unrealistic and unstable. More reasonable allocations are obtained with the Black-Litterman (BL) approach to combining subjective views with equilibrium returns. Then both hedge funds instruments play a significant role in optimal passive portfolios if their expected excess returns are at least 1%. Long volatility positions are also likely to be attractive. However the BL approach can also be criticised.hedge funds, investable hedge funds indices, funds of hedge funds, commodities, VIX, mean-variance analysis, Sharpe Ratio, Adjusted Sharpe Ratio, Omega Ratio, Black Litterman model

S&P 500 index effect reconsidered: Evidence from overnight and intraday stock prices performance and volume

Abstract This study focuses on S&P500 inclusions and deletions, examining the impact of potential overnight price adjustment after the announcement of an S&P500 index change. We find evidence of a significant overnight price change that diminishes the returns available to speculators although there are still profits available from the first day after announcement until a few days after the actual event. More importantly, observing the tick-by-tick stock price performance and volume effects on the key days during the event window for the first time, we find evidence of consistent trading patterns during trading hours. A separate analysis of NASDAQ and NYSE listed stocks allows for a detailed examination of the price and volume effect at an intra-day level. We find that index funds appear to cluster their rebalancing activities near to and after the close on the event date, suggesting that they are more concerned with tracking error than profit. JEL classification: G10; G14 Keywords: Index effect; S&P500; Market efficiency; Price pressure * Corresponding author. Tel.: +44 118 378 7809; fax: +44 118 931 4741. E-mail addresses: [email protected] (K.Kappou), [email protected] (C. Brooks), [email protected] (C. Ward). Acknowledgements: We are grateful to two anonymous referees for useful comments that considerably re-shaped and improved this paper. We would also like to thank S&P Corporation for providing information on the announcement and effective dates of S&P500 inclusions. We are grateful t