Marco Nicolosi

The Cost of Sustainability in Optimal Portfolio Decisions

We examined the impact of including sustainability-related constraints in optimal portfolio decision-making. Our analysis covered an investment set containing the components of the S&P500 index from 1993 to 2008. Optimizations were performed according to the classic mean–variance approach, while sustainability constraints were introduced by eliminating, from the investment pool, those assets that do not comply with the given social responsibility criteria (screening). We compared the efficient frontiers with and without screening. The analysis focused on the three main dimensions of sustainability, namely the environmental, social and governance ones. We found that socially responsible screening gives rise to a small loss in terms of the Sharpe ratio even though it has a great impact on the market capitalization of the optimal portfolio. The spanning test showed that the ex-post differences between the two frontiers, when short selling is not allowed, are significant only in the case of environmental screening.

Portfolio management with benchmark related incentives under mean reverting processes

We study the problem of a fund manager whose compensation depends on the relative performance with respect to a benchmark index. In particular, the fund manager’s risk-taking incentives are induced by an increasing and convex relationship of fund flows to relative performance. We consider a dynamically complete market with N risky assets and the money market account, where the dynamics of the risky assets exhibit mean reversions, either in the drift or in the volatility. The manager optimizes the expected utility of the final wealth, with an objective function that is non-concave. The optimal solution is found by using the martingale approach and a concavification method. The optimal wealth and the optimal strategy are determined by solving a system of Riccati equations. We provide a semi-closed solution based on the Fourier transform.

Item Response Models to Measure Corporate Social Responsibility

Corporate social responsibility (CSR) is a multidimensional concept that involves several aspects, ranging from environment to social and governance. Companies aiming to comply with CSR standards have to face challenges that vary from one aspect to the other and from one industry to the other. Latent variable models may be usefully employed to provide a unidimensional measure of the grade of compliance of a firm with CSR standards, which is both understandable and theoretically solid. A methodology based on item response theory has been implemented on the multidimensional sustainability rating as expressed by KLD data-set from 1991 to 2007. Results suggest that companies in the oil and gas industry together with firms in industrials, basic materials and telecommunications have a higher difficulty to meet the CSR standards. Criteria based on human rights, environment, community and product quality have a large capacity to select the best performing firms, as they are very discriminant, while governance does not exhibit similar behaviour. A stock selection based on the ranking of the firms according to the proposed CSR measure supports the hypothesis of a positive relationship between CSR and financial performance.

On the Effect of Skewness and Kurtosis Misspecification on the Hedging Error

Using a result in Angelini and Herzel (2009a), we measure, in terms of variance, the cost of hedging a contingent claim when the hedging portfolio is re-balanced at a discrete set of dates. We analyse the dependence of the variance of the hedging error on the skewness and kurtosis as modeled by a Normal Inverse Gaussian model. We consider two types of strategies, the standard Black-Scholes Delta strategy and the locally variance-optimal strategy, and we perform some robustness tests. In particular, we investigate the effect of different types of model misspecification on the performance of the hedging, like that of hedging without taking skewness into account. Computations are performed using a Fast Fourier Transform approach.

Dynamic portfolio management with views at multiple horizons

We introduce Dynamic Entropy Pooling, a quantitative technique to perform dynamic portfolio construction with discretionary, non-synchronous views. With Dynamic Entropy Pooling, the portfolio manager can embed in the allocation process subjective views with life spans ranging from minutes to years, calendar views, autocorrelation stress-testing, and the traditional views on expectations, correlations and volatilities.After introducing the theoretical framework for Dynamic Entropy Pooling, we show how to solve the respective portfolio construction problem by means of dynamic programming with time-dependent coefficients. To understand the optimal exposures ensuing from Dynamic Entropy Pooling we analyze a variety of relevant sub-cases and we present some case-studies.

A Socially Responsible Portfolio Selection Strategy

We propose a new methodology to integrate Socially Responsible (SR) standards in the process of investment decisions. We use SR scores of companies in the S&P500 and in the Domini Social Index (DSI) to define the level of SR of a portfolio. We model this as a linear combination of the SR scores of the single stocks with coefficients given by the portfolio’s weights. We form portfolios that minimize the tracking error from the DSI while improving the SR level. The analysis of the performances of the portfolios show that the improvement of the SR is usually possible at a small cost in terms of tracking error, and that the improved portfolios produced, in most of the cases, better financial performances than the benchmark.

The Value of Information for Optimal Portfolio Management

We study the value of information for a manager who invests in a stock market to optimize the utility of her future wealth. We consider an incomplete financial market model with a mean reverting market price of risk that cannot be directly observed by the manager. The available information is represented by the filtration generated by the stock price process. We solve the classical Merton problem for an incomplete market under partial information by means of filtering techniques and the martingale approach.

Optimal strategies with option compensation under mean reverting returns or volatilities

We study the problem of a fund manager whose contractual incentive is given by the sum of a constant and a variable term. The manager has a power utility function and the continuous time stochastic processes driving the dynamics of the market prices exhibit mean reversion either in the volatilities or in the expected returns. We provide an approximation for the optimal wealth and for the optimal strategy based on affine processes and the fast Fourier transform.