Portfolio Optimization and Performance Analysis

Prigent, Jean‐Luc

doi:10.1201/9781420010930

Cited by 86 publications

(53 citation statements)

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“…This section extends the results in Leland (1980), Brennan and Solanki (1981), Carr and Madan (2001) and Prigent (2007) to RDEU framework. Suppose that the investor maximizes an expectation of his utility U with possible modification of probabilities (Note 2).…”

Section: Optimal Positioning Of Structured Fundsmentioning

confidence: 87%

“…In a continuous-time framework, Merton (1971) determines the optimal portfolio for various utility functions. These results have been further extended in various directions, taking account for example of financial market incompleteness, of specific constraints on portfolio weights, of labor income, of random horizon...as in Cox and Huang (1989), Cvitanic and Karatzas (1996), and, with insurance constraints, El Karoui et al (2005) and Prigent (2006) (see also Campbell and Viceira, 2002;Prigent, 2007, for a survey about such results). However, some well documented paradoxes, such as the Allais's paradox, have proved that standard utility theory does not model actual behaviour towards risk.…”

Section: Introductionmentioning

confidence: 99%

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Behaviour towards Risk in Structured Portfolio Management

Ameur¹,

Prigent²

2010

IJEF

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We examine the optimal design of financial structured portfolios (equity or index linked notes) within the rank dependent utility framework. We illustrate how these products can be in accordance to investor's attitude towards risk, whereas, for the standard expected utility case, they do not match investor's preferences. These financial products usually involve derivative instruments which allow investors to benefit from capital protection and minimal participation when markets are bullish.

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Section: Optimal Positioning Of Structured Fundsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Behaviour towards Risk in Structured Portfolio Management

Ameur¹,

Prigent²

2010

IJEF

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“…The annualized conditional Sharpe ratio (Eling and Schuhmacher, 2007) is computed at the 99% level, and the annualized mean excess return is the mean of the asset minus liability returns. The Sortino ratio is the mean return on the asset-liability portfolio, divided by the standard deviation of returns on the asset-liability portfolio computed using only negative returns, with the minimum acceptable return set to zero (Prigent, 2007). The Dowd ratio is the mean return on the asset-liability portfolio, divided by the value at risk for a chosen confidence level (deflated by the initial value of the asset-liability portfolio), (Prigent, 2007).…”

Section: Resultsmentioning

confidence: 99%

“…The Sortino ratio is the mean return on the asset-liability portfolio, divided by the standard deviation of returns on the asset-liability portfolio computed using only negative returns, with the minimum acceptable return set to zero (Prigent, 2007). The Dowd ratio is the mean return on the asset-liability portfolio, divided by the value at risk for a chosen confidence level (deflated by the initial value of the asset-liability portfolio), (Prigent, 2007). We used the 99% confidence level for the value at risk when computing the Dowd ratio, and have also included the value at risk and the conditional value at risk, both at the 99% level, in Table 7 as measures of tail risk.…”

Section: Resultsmentioning

confidence: 99%

Asset Liability Modelling and Pension Schemes: The Application of Robust Optimization to USS

Platanakis

Sutcliffe

2014

SSRN Journal

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Asset Liability Modelling and Pension Schemes: the Application of Robust Optimization to USS AbstractThis paper uses a novel numerical optimization technique -robust optimization -that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components -active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme's projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, BayesStein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation -essentially fix-mix. These conclusions are supported by six robustness checks.

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“…Instead of the squared deviations g 2 (π), the absolute error g 1 (π) is also a popular choice in the literature (e.g., [10,20,22]). This is advantageous in that the optimization problem is reduced to a linear program, which can be efficiently solved.…”

Section: Portfolio Selection By Index Trackingmentioning

confidence: 99%

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios

et al. 2012

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Index tracking is a passive investment strategy in which an investor purchases a set of assets to mimic a market index. The tracking error, the difference between the performances of the index and the portfolio, may be minimized by buying all the assets contained in the index. However, this strategy results in a considerable amount of transaction cost and, accordingly, decreases the return of the constructed portfolio. On the other hand, a portfolio with small cardinality may result in a poor out-of-sample performance. Of interest is, thus, the minimization of tracking error, while keeping the number of assets invested in small (i.e., sparse).In this paper, we develop a tracking portfolio model that addresses the conflicting requirements above by a combination of L0-and L2-norms. While L2-norm regularizes the overdetermined system to impose smoothness (and hence better out-of-sample) performance and shrinks the solution to the equally-weighted dense portfolio, L0-norm imposes a cardinality constraint that achieves sparsity (and hence lower transaction cost). We propose a heuristic method for parameter estimation in this model, which combines greedy search with an analytical formula embedded in it. We demonstrate that a sparse portfolio can achieve good tracking and generalization performance on historic data of weekly and daily returns on the Nikkei 225 index and its constituent companies.

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Portfolio Optimization and Performance Analysis

Cited by 86 publications

References 0 publications

Behaviour towards Risk in Structured Portfolio Management

Behaviour towards Risk in Structured Portfolio Management

Asset Liability Modelling and Pension Schemes: The Application of Robust Optimization to USS

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios

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