Portfolio & Risk Management: Asset Allocation and Risk Budgeting Optimization

D. Widijanto and S. Nagornii

No 160, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: In standard static Mean-Variance approach portfolio is presented by one allocation vector optimized in terms of expected returns & variance-covariance (VcV) matrix. Such one-dimensional approach is not suitable for Fixed Income: i) portfolio cannot be described by allocation vector only, and ii) returns VcV matrix is period dependent even if yield process is stationary. Multi-dimensional optimization problem is formulated in terms of risk-sensitivity matrix (RSM), allocation & yield vectors. Yield vector reflects term-structure, security & asset selection. Returns VcV matrix is expressed in terms of yield VcV matrix and RSM, which is specified by risk budgeting & duration management. So, optimal allocation is conditional on RSM, i.e. on risk & portfolio management strategies. Instantly efficient portfolio derived from static one-dimensional optimization will not be efficient after infinitesimal time transformation since RSM is not time-invariant. Multi-dimensional approach provides optimal allocation and duration management strategies for any risk budgeting constraint. An optimal RSM allows for a minimum Tracking Error portfolio to be more efficient than benchmark"s global efficient frontier. Infinite amount of optimal portfolio alteration decisions, subject of RSM choice, is benchmark dependent. General conclusion: integrated portfolio & risk management process is an important issue for asset management.

Keywords: Asset Allocation; Risk Budgeting; Portfolio Optimization; Risk Management; Risk Sensitivity; Variance Covariance; Tracking Error; Yield Vector; Allocation Vector; Term Structure; Multi-Dimensional Optimization (search for similar items in EconPapers)
JEL-codes: C40 C41 G10 G11 (search for similar items in EconPapers)
Date: 2004-08-11
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:160

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