Enhanced indexation based on second-order stochastic dominance

Second order Stochastic Dominance (SSD) has a well recognised importance in portfolio selection, since it provides a natural interpretation of the theory of risk-Averse investor behaviour. Recently, SSD-based models of portfolio choice have been proposed; these assume that a reference distribution is available and a portfolio is constructed, whose return distribution dominates the reference distribution with respect to SSD. We present an empirical study which analyses the effectiveness of such strategies in the context of enhanced indexation. Several datasets, drawn from FTSE 100, SP 500 and Nikkei 225 are investigated through portfolio rebalancing and backtesting. Three main conclusions are drawn. First, the portfolios chosen by the SSD based models consistently outperformed the indices and the traditional index trackers. Secondly, the SSD based models do not require imposition of cardinality constraints since naturally a small number of stocks are selected. Thus, they do not present the computational difficulty normally associated with index tracking models. Finally, the SSD based models are robust with respect to small changes in the scenario set and little or no rebalancing is necessary. In this paper we present a unified framework which incorporates (a) SSD, (b) downside risk (Conditional Value-At-Risk) minimisation and (c) enhanced indexation. © 2013 Elsevier B.V. All rights reserved

Index tracking with utility enhanced weighting

Passive index investing involves investing in a fund that replicates a market index. Enhanced indexation uses the returns of an index as a reference point and aims at outperforming this index. The motivation behind enhanced indexing is that the indices and portfolios available to academics and practitioners for asset pricing and benchmarking are generally inefficient and, thus, susceptible to enhancement. In this paper we propose a novel technique based on the concept of cumulative utility area ratios and the Analytic Hierarchy Process (AHP) to construct enhanced indices from the DJIA and S&P500. Four main conclusions are forthcoming. First, the technique, called the utility enhanced tracking technique (UETT), is computationally parsimonious and applicable for all return distributions. Second, if desired, cardinality constraints are simple and computationally parsimonious. Third, the technique requires only infrequent rebalancing, monthly at the most. Finally, the UETT portfolios generate consistently higher out-of-sample utility profiles and after-cost returns for the fully enhanced portfolios as well as for the enhanced portfolios adjusted for cardinality constraints. These results are robust to varying market conditions and a range of utility functions

Enhanced index tracking with CVaR-based ratio measures

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Forecast Combination Based on Multiple Encompassing Tests in a Macroeconomic DSGE System

We use data generated by a macroeconomic DSGE model to study the relative benefits of forecast combinations based on forecast-encompassing tests relative to simple uniformly weighted forecast averages across rival models. Assumed rival models are four linear autoregressive specifications, one of them a more sophisticated factor-augmented vector autoregression (FAVAR). The forecaster is assumed not to know the true data-generating DSGE model. The results critically depend on the prediction horizon. While one-step prediction hardly supports test-based combinations, the test-based procedure attains a clear lead at prediction horizons greater than two.Combining forecasts, encompassing tests, model selection, time series, DSGE model

Novel approaches for portfolio construction using second order stochastic dominance

In the last decade, a few models of portfolio construction have been proposed which apply Second Order Stochastic Dominance (SSD) as a choice criterion. SSD approach requires the use of a reference distribution which acts as a benchmark. The return distribution of the computed portfolio dominates the benchmark by the SSD criterion. The benchmark distribution naturally plays an important role since di erent benchmarks lead to very di erent portfolio solutions. In this paper we describe a novel concept of reshaping the benchmark distribution with a view to obtaining portfolio solutions which have enhanced return distributions. The return distribution of the constructed portfolio is considered enhanced if the left tail is improved, the downside risk is reduced and the standard deviation remains within a speci ed range. We extend this approach from long only to long-short strategies which are used by many hedge fund and quant fund practitioners. We present computational results which illustrate (i) how this approach leads to superior portfolio performance (ii) how signi cantly better performance is achieved for portfolios that include shorting of assets

A heuristic framework for the bi-objective enhanced index tracking problem

The index tracking problem is the problem of determining a portfolio of assets whose performance replicates, as closely as possible, that of a financial market index chosen as benchmark. In the enhanced index tracking problem the portfolio is expected to outperform the benchmark with minimal additional risk. In this paper, we study the bi-objective enhanced index tracking problem where two competing objectives, i.e., the expected excess return of the portfolio over the benchmark and the tracking error, are taken into consideration. A bi-objective Mixed Integer Linear Programming formulation for the problem is proposed. Computational results on a set of benchmark instances are given, along with a detailed out-of-sample analysis of the performance of the optimal portfolios selected by the proposed model. Then, a heuristic procedure is designed to build an approximation of the set of Pareto optimal solutions. We test the proposed procedure on a reference set of Pareto optimal solutions. Computational results show that the procedure is significantly faster than the exact computation and provides an extremely accurate approximation

Methods for solving problems in financial portfolio construction, index tracking and enhanced indexation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe focus of this thesis is on index tracking that aims to replicate the movements of an index of a specific financial market. It is a form of passive portfolio (fund) management that attempts to mirror the performance of a specific index and generate returns that are equal to those of the index, but without purchasing all of the stocks that make up the index. Additionally, we consider the problem of out-performing the index - Enhanced Indexation. It attempts to generate modest excess returns compared to the index. Enhanced indexation is related to index tracking in that it is a relative return strategy. One seeks a portfolio that will achieve more than the return given by the index (excess return). In the first approach, we propose two models for the objective function associated with choice of a tracking portfolio, namely; minimise the maximum absolute difference between the tracking portfolio return and index return and minimise the average of the absolute differences between tracking portfolio return and index return. We illustrate and investigate the performance of our models from two perspectives; namely, under the exclusion and inclusion of fixed and variable costs associated with buying or selling each stock. The second approach studied is that of using Quantile regression for both index tracking and enhanced indexation. We present a mixed-integer linear programming of these problems based on quantile regression. The third approach considered is on quantifying the level of uncertainty associated with the portfolio selected. The quantification of uncertainty is of importance as this provides investors with an indication of the degree of risk that can be expected as a result of holding the selected portfolio over the holding period. Here a bootstrap approach is employed to quantify the uncertainty of the portfolio selected from our quantile regression model.CARISM

Two-Country Models of Monetary and Fiscal Policy: What Have We Learned? What More Can We Learn?

This paper surveys the literature that uses two-country models to analyze monetary and fiscal policy issues faced in interdependent economies. We discuss sources of structural interdependence that researchers typically include in these models. We describe many of the types of policy interactions that researchers have considered and summarize the key results that they have obtained. Finally, we briefly explain the limitations of two-country models and outline directions that this literature might usefully be extended

Index tracking with utility enhanced weighting

Passive index investing involves investing in a fund that replicates a market index. Enhanced indexation uses the returns of an index as a reference point and aims at outperforming this index. The motivation behind enhanced indexing is that the indices and portfolios available to academics and practitioners for asset pricing and benchmarking are generally inefficient and, thus, susceptible to enhancement. In this paper we propose a novel technique based on the concept of cumulative utility area ratios and the Analytic Hierarchy Process (AHP) to construct enhanced indices from the DJIA and S&P500. Four main conclusions are forthcoming. First, the technique, called the utility enhanced tracking technique (UETT), is computationally parsimonious and applicable for all return distributions. Second, if desired, cardinality constraints are simple and computationally parsimonious. Third, the technique requires only infrequent rebalancing, monthly at the most. Finally, the UETT portfolios generate consistently higher out-of-sample utility profiles and after-cost returns for the fully enhanced portfolios as well as for the enhanced portfolios adjusted for cardinality constraints. These results are robust to varying market conditions and a range of utility functions

Multidimensional Poverty Rankings based on Pareto Principle: A Practical Extension

This paper proposes a ranking method of multidimensional poverty and extends it aiming to enhance its practical utility. While our original ranking method that assumes non-comparability among different dimensions of poverty succeeds in eliminating some implicit arbitrariness in existing ranking, it also confronts a disadvantage that a non- negligible number of objectives (countries) are ranked at the same level. In order to improve this disadvantage, we propose an extended ranking method, where we allow the data to have a certain range of bandwidth. The introduction of bandwidth improves the usefulness of our ranking in the sense that it decreases the number of countries with the same rank. In addition, a simulation exercise shows that this extension also improves the robustness of the ranking against measurement errors.