Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns

Joel  Goh; Kian Guan  Lim; Melvyn  Sim; Weina  Zhang

Article | European Journal of Operational Research

Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns

by Joel Goh, Kian Guan Lim, Melvyn Sim and Weina Zhang

Abstract

We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using simulated data, the PVaR approach always generates better risk-return tradeoffs in the optimal portfolios when compared to traditional Markowitz mean–variance approach. When using real financial data, our approach also outperforms the Markowitz approach in the risk-return tradeoff. Given that the PVaR measure is also a robust risk measure, our new approach can be very useful for optimal portfolio allocations when asset return distributions are asymmetrical.

Keywords: robust optimization; portfolio management; value-at-risk; Mathematical Methods; Finance;

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Goh, Joel, Kian Guan Lim, Melvyn Sim, and Weina Zhang. "Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns." European Journal of Operational Research 221, no. 2 (September 1, 2012): 397–406.

Article | Annals of Internal Medicine | 2010

Estimating the Attributable Cost of Physician Burnout in the United States

Shasha Han, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel and Joel Goh

Background: Although physician burnout is associated with negative clinical and organizational outcomes, its economic costs are poorly understood. As a result, leaders in health care cannot properly assess the financial benefits of initiatives to remediate physician burnout.
Objective: To estimate burnout-associated costs related to physician turnover and physicians reducing their clinical hours at national (U.S.) and organizational levels.
Design: Cost-consequence analysis using a mathematical model.
Setting: United States.
Participants: Simulated population of U.S. physicians.
Measurements: Model inputs were estimated by using the results of contemporary published research findings and industry reports.
Results: On a national scale, the conservative base-case model estimates that approximately $4.6 billion in costs related to physician turnover and reduced clinical hours is attributable to burnout each year in the United States. This estimate ranged from $2.6 billion to $6.3 billion in multivariate probabilistic sensitivity analyses. At an organizational level, the annual economic cost associated with burnout related to turnover and reduced clinical hours is approximately $7600 per employed physician each year.
Limitations: Possibility of nonresponse bias and incomplete control of confounders in source data. Some parameters were unavailable from data and had to be extrapolated.
Conclusion: Together with previous evidence that burnout can effectively be reduced with moderate levels of investment, these findings suggest substantial economic value for policy and organizational expenditures for burnout reduction programs for physicians.

Keywords: Physicians; burnout; Health; Health Care and Treatment; Employees; Cost; Programs; Policy; Health Industry;

Citation:

Han, Shasha, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel, and Joel Goh. "Estimating the Attributable Cost of Physician Burnout in the United States." Annals of Internal Medicine 170, no. 11 (June 4, 2019): 784–790. View Details

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Recent Medicare legislation seeks to improve patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates and instead relies on providers accurately self-reporting HAIs in claims to correctly assess penalties. Consequently, the incentives for providers to improve service quality may disappear if providers upcode, i.e., misreport HAIs (possibly unintentionally) in a manner that increases reimbursement or avoids financial penalties. Identifying upcoding in claims data is challenging due to unobservable confounders (e.g., patient risk). We leverage state-level variations in adverse event reporting regulations and instrumental variables to discover contradictions in HAI and present-on-admission (POA) infection reporting rates that are strongly suggestive of upcoding. We conservatively estimate that 10,000 out of 60,000 annual reimbursed claims for POA infections (18.5%) were upcoded HAIs, costing Medicare $200 million. Our findings suggest that self-reported quality metrics are unreliable and thus, recent legislation may result in unintended consequences.

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Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns

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