Value-at-risk in portfolio optimization: properties and computational approach

Gaivoronski, Alexei A.; Pflug, Georg Ch.

doi:10.21314/jor.2005.106

Cited by 274 publications

(141 citation statements)

References 32 publications

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“…Heuristics for optimizing sample VaR have been proposed in Gaivoronski and Pflug [17] and Larsen et al [23]. El Ghaoui et al [15] suggest a probabilistic approach that optimizes the VaR for the worst-case distribution of the unknown asset returns.…”

Section: Value-at-risk (Var)mentioning

confidence: 99%

Constructing Risk Measures from Uncertainty Sets

Natarajan

Pachamanova

Sim

2009

Operations Research

152

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We propose a unified theory that links uncertainty sets in robust optimization to risk measures in portfolio optimization. We illustrate the correspondence between uncertainty sets and some popular risk measures in finance, and show how robust optimization can be used to generalize the concepts of these measures. We also show that by using properly defined uncertainty sets in robust optimization models, one can in fact construct coherent risk measures. Our approach to creating coherent risk measures is easy to apply in practice, and computational experiments suggest that it may lead to superior portfolio performance. Our results have implications for efficient portfolio optimization under different measures of risk.

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Section: Value-at-risk (Var)mentioning

confidence: 99%

Constructing Risk Measures from Uncertainty Sets

Natarajan

Pachamanova

Sim

2009

Operations Research

152

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“…A reinsurer can derive important portfolio risk metrics such as the Probable Maximum Loss (PML) [30] and the Tail Value-at-Risk (TVaR) [31] which are used for both internal risk management and reporting to regulators and rating agencies. Furthermore, these metrics flow into a final stage of the risk analytics pipeline, namely Enterprise Risk Management, where liability, asset, and other forms of risks are combined and correlated to generate an enterprise wide view of risk.…”

Section: Typical Layer Covers Approximately 3 To 30 Individual Elts Amentioning

confidence: 99%

Multi-tenant virtual GPUs for optimising performance of a financial risk application

Prades

Varghese

Reaño

et al. 2017

Journal of Parallel and Distributed Computing

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Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

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“…Fabozzi et al, (2004) propose clustering as a methodology to construct a tracking portfolio. Gaivoronski and Pflug (2005) uses VaR as risk measure and introduces/sets forth mean-VaR frontiers. Konno et al (2005) present a branch and bound algorithm for constructing or rebalancing a portfolio and use absolute deviations of returns rates.…”

Section: Introductionmentioning

confidence: 99%

A Robust Optimization Approach for Index Tracking Problem

Gharakhani¹,

Fazlelahi²,

Sadjadi³

2014

Journal of Computer Science

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Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this study, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this study employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g., information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.

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Value-at-risk in portfolio optimization: properties and computational approach

Cited by 274 publications

References 32 publications

Constructing Risk Measures from Uncertainty Sets

Constructing Risk Measures from Uncertainty Sets

Multi-tenant virtual GPUs for optimising performance of a financial risk application

A Robust Optimization Approach for Index Tracking Problem

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