Unit – III Session No. 26 Topic: Optimization Portfolio Management Unit – III Session No. 26 Topic: Optimization

Session Plan Recap the previous session Optimization Mean-Variance Approach Resampled Efficient Frontier Black-Litterman Approach Monte Carlo Simulation ALM Summarizing and Q & A

Optimization A critical step in strategic asset allocation is the procedure we use for converting the inputs to a specific recommended strategic asset allocation. Focused on developing and refining a variety of procedures. Established procedures have a quantitative flavor. Investment advisers, particularly those serving an individual investor clientele, may use a qualitative approach based on experience. All professional investors apply judgment in making recommendations.

Optimization Optimization Approaches: Mean-Variance Approaches The Efficient Frontier Resampled Efficient Frontier Black-Litterman Approach Monte Carlo Simulation ALM

Optimization The Mean–Variance Approach Mean–variance analysis provided the first, and still important, quantitative approach to strategic asset allocation. A strategic asset allocation suggested by mean–variance analysis should be subjected to professional judgment before adoption. Return and Risk are the factors used for this analysis

Optimization The Efficient Frontier According to mean–variance theory, in determining a strategic asset allocation an investor should choose from among the efficient portfolios consistent with that investor’s risk tolerance. Efficient portfolios make efficient use of risk; they offer the maximum expected return for their level of variance or standard deviation of return. Efficient portfolios plot graphically on the efficient frontier, which is part of the minimum-variance frontier (MVF). Each portfolio on the minimum-variance frontier represents the portfolio with the smallest variance of return for its level of expected return. The graph of a minimum-variance frontier has a turning point (its leftmost point) that represents the global minimum-variance (GMV) portfolio. The GMV portfolio has the smallest variance of all minimum-variance portfolios. The portion of the minimum-variance frontier beginning with and continuing above the GMV portfolio is the efficient frontier.

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Optimization The Resampled Efficient Frontier By assumption, using sample values of Asset Classes, means, variances and covariance's, the simulation(reproduction) generates sets of simulated returns for each sets based on weights is known as simulated efficient portfolios. Information in simulated efficient portfolios is integrated into one frontier called the resampled efficient frontier. In simple terms, “ the set of resampled efficient portfolios represents the resampled efficient frontier”.

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Optimization However, there are issues: The Resampled Efficient Frontier Resampling provides an improvement over traditional methods However, there are issues: Average of maximums is not the maximum Hence, allocation will be suboptimal and we should be able to improve on this work New research on the horizon that provides an alternative.

Optimization The Black–Litterman Approach Fischer Black and Robert Litterman developed another quantitative approach to dealing with the problem of estimation error, which we recall is most serious when it concerns expected returns. Two versions of the Black–Litterman approach exist: 1. Unconstrained Black–Litterman (UBL) model . Taking the weights of asset classes in a global benchmark such as MSCI World as a neutral starting point, the asset weights are adjusted to reflect the investor’s views on the expected returns of asset classes according to a Bayesian procedure that considers the strength of the investor’s beliefs. The procedure does not allow non-negativity constraints on the asset-class weights. 2. Black–Litterman (BL) model. This approach reverse engineers the expected returns implicit in a diversified market portfolio (a process known as reverse optimization) and combines them with the investor’s own views on expected returns in a systematic way that takes into account the investor’s confidence in his or her views. These view-adjusted expected return forecasts are then used in a MVO with a constraint against short sales and possibly other constraints.

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Optimization Monte Carlo Simulation Monte Carlo simulation, a computer-based technique, has become an essential tool in many areas of investments. Monte Carlo simulation involves the calculation and statistical description of the outcomes resulting in a particular strategic asset allocation under random scenarios for investment returns, inflation, and other relevant variables. The method provides information about the range of possible investment results from a given asset allocation over the course of the investor’s time horizon, as well as the likelihood that each result will occur. Monte Carlo simulation contrasts to and complements MVO. Standard MVO is an analytical methodology based on calculus. By contrast, Monte Carlo simulation is a statistical tool. Monte Carlo simulation imitates (simulates) an asset allocation’s real-world operation in an investments laboratory, where the investment adviser incorporates his best understanding of the set of relevant variables and their statistical properties.

Optimization Asset/Liability Management Approach for managing risks that arise due to mismatches between the assets and liabilities. It is not just about offering solutions to mitigate or hedge the risks arising from the interaction of assets and liabilities It is focused on a long-term perspective. Success in the process of maximizing assets to meet complex liabilities may increase profitability. An asset portfolio is meant to fund a specified liability schedule (funding a liability means being able to pay the liability when it comes due). Such cases call for an ALM approach. Using an ALM approach, asset allocation must consider the risk characteristics of the liabilities in addition to those of the assets, because the focus is on funding the liabilities. Earlier we presented the efficient frontier. That efficient frontier is more precisely the ‘‘asset-only’’ efficient frontier, because it fails to consider liabilities. Net worth (the difference between the market value of assets and liabilities), also called surplus, summarizes the interaction of assets and liabilities in a single variable. The ALM perspective focuses on the surplus efficient frontier. Mean–variance surplus optimization extends traditional MVO to incorporate the investor’s liabilities.

Optimization What is the need of Optimization? What is the use of efficient frontier? What is MVO? What is the limitation of the mean-variance approach? How efficient portfolio helps the investor’s to formulate risk and return? How resampled efficient portfolio approach is different from MVO? What is the use of BL model? What is reverse optimization? How Monte Carlo is distinct from other models? Why ALM approach has been used for effective determination of optimization?