【導(dǎo)讀】ChapterOpener. p.926. Table

　　

【正文】 out in previous chapters. It can go a long way in forging sound underpinnings to the daily work of portfolio management. A few ments are in order, though.p. 937Figure Organizational chart for portfolio managementSource: Adapted from Robert C. Merton, Finance Theory, Chapter 12, Harvard Business School.The control units responsible for forecasting records and determining forecast adjustments will directly affect the advancement and bonuses of security analysts and estimation experts. This implies that these units must be independent and insulated from organizational pressures.An important issue is the conflict between independence of security analysts39。 opinions and the need for cooperation and coordination in the use of resources and contacts with corporate and government personnel. The relative size of the security analysis unit will further plicate the solution to this conflict. In contrast, the macro forecast unit might bee too insulated from the security analysis unit. An effort to create an interface and channels of munications between these units is warranted.Finally, econometric techniques that are invaluable to the organization have seen a quantum leap in sophistication in recent years, and this process seems still to be accelerating. It is critical to keep the units that deal with estimation updated and on top of the latest developments.2In application to debates about social issues, you might define a fanatic as one who enters the debate with a prior that is so tight that no argument will influence his posterior, making the debate altogether a waste of time.3Jack Treynor and Fischer Black, “How to Use Security Analysis to Improve Portfolio Selection,” Journal of Business, January 1973.4Alex Kane, TaeHwan Kim, and Halbert White, “Active Portfolio Management: The Power of the TreynorBlack Model,” in Progress in Financial Market Research, ed. C. Kyrtsou (New York: Nova, 2004).5These constraints on forecasts make sense because on an annual basis they imply a stock would rise by more than 380% or fall below 22% of its beginningofyear value. The BlackLitterman ModelFischer Black, famous for the TreynorBlack model as well as the BlackScholes optionpricing formula, teamed up with Robert Litterman to produce another useful model that allows portfolio managers to quantify plex forecasts (which they call views An analyst39。s opinion on the likely performance of a stock or sector pared to the marketconsensus expectation.) and apply these views to portfolio We begin the discussion of their model with an illustration of a simple problem of asset allocation. Although we devote the next section to a parison of the two models, some ments on monalities of the models will help us better understand the BlackLitterman (BL) model.p. 938A Simple Asset Allocation DecisionConsider a portfolio manager laboring over asset allocation Choosing among broad asset classes such as stocks versus bonds. to bills, bonds, and stocks for the next month. The risky portfolio will be constructed from bonds and stocks so as to maximize the Sharpe ratio. The optimal risky portfolio is the tangency portfolio to the capital allocation line (CAL). Investors in the manager39。s fund will choose desired positions along the CAL, that is, binations of bills and the optimal risky portfolio, based on their degree of risk aversion. So far this is no more than the problem described in Section of Chapter 7. There, we were concerned with optimizing the portfolio given a set of data inputs. In real life, however, optimization using a given data set is the least of the manager39。s problems. The real issue that dogs any portfolio manager is how to e by that input data. Black and Litterman propose an approach that uses past data, equilibrium considerations, and the private views of the portfolio manager about the near future.These days, past returns on bond and stock portfolios (in fact, virtually any asset of interest) are readily available. The question is how to use those historical returns. The statistics we usually focus on are historical average returns and an estimate of the covariance matrix. But there is a critical difference between these two statistics. The great variability of returns, especially over short horizons, implies that returns over the ing month can barely be forecast. Table (Chapter 5) demonstrated that even multiyear average returns fluctuate markedly. Surely the present state of the business cycle and other macroeconomic conditions dominate expected returns for the next month. In contrast, we can take a recent sample of returns and slice it into short holding periods to obtain a reasonably accurate forecast of the covariance matrix for next month.Step 1: The Covariance Matrix from Historical DataThis straightforward task is the first in the chain that makes up the BL model. Suppose step 1 results in the following annualized covariance matrix, estimated from recent historical excess returns: Notice that step 1 is mon to both the BL and the TreynorBlack (TB) models. This activity appears in the organizational chart in Figure .Step 2: Determination of a Baseline ForecastBecause past data are of such limited use in inferring expected returns for the next month, BL propose an alternative approach. They start with a baseline forecast Forecast of security returns derived from the assumption that the market is in equilibrium where current prices reflect all available information. derived from the assumption that the market is in equilibrium where current prices of stocks and bonds reflect all available information and, as a result, the theoretical market portfolio with weights equal to marketvalue proportions is efficient. Suppose that current market values of outstanding bonds and stocks imply that the weight of bonds in the baseline portfolio is wB = .25, and the weight of stocks is wS = .75. When we apply these portfolio weights to the covariance matrix from step 1, the varianc