Downside Risk Premium

2021 ◽  
pp. 138-147
Author(s):  
Kanellos Stylianou Toudas
Keyword(s):  
Portfolio Management ◽  
Risk Premium ◽  
Critical Reflection ◽  
Risk Measure ◽  
Linear Optimization ◽  
Downside Risk ◽  
Optimization Techniques ◽  
Portfolio Efficiency ◽  
Recent Developments ◽  
Safety First

The purpose of this chapter is to address the main developments and challenges on risk assessment and portfolio management. The former innovation in modern portfolio theory, Markowitz, has been succeeded from linear and non-linear optimization techniques that improve portfolio efficiency. Special emphasis is given on Roy's seminal work on “Safety First Criterion” which advocates that the safety of investments should be prioritized. Thus, an investment should be chosen in a way that it has the lowest probability of falling short of a required threshold of investors. This motivated Markowitz to advocate a downside risk measure based on semivariance. It captures the notion of risk as failure to meet some minimum target. It is influenced by returns below the target rate. It focuses on investors' concern with downside variability and loss reduction. This chapter offers a critical reflection of these recent developments and could be of interest for individual and institutional investors.

scholarly journals A Numerical Study for Robust Active Portfolio Management with Worst-Case Downside Risk Measure

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Aifan Ling ◽  
Le Tang
Keyword(s):  
Portfolio Management ◽  
Numerical Study ◽  
Risk Measure ◽  
Tracking Error ◽  
Downside Risk ◽  
Worst Case ◽  
Active Portfolio Management ◽  
Multiple Weights ◽  
Real Market ◽  
Portfolio Selection Model

Recently, active portfolio management problems are paid close attention by many researchers due to the explosion of fund industries. We consider a numerical study of a robust active portfolio selection model with downside risk and multiple weights constraints in this paper. We compare the numerical performance of solutions with the classical mean-variance tracking error model and the naive1/Nportfolio strategy by real market data from China market and other markets. We find from the numerical results that the tested active models are more attractive and robust than the compared models.

CAPITAL ALLOCATION WITH MULTIVARIATE RISK MEASURES: AN AXIOMATIC APPROACH

2019 ◽  
Vol 34 (2) ◽  
pp. 297-315
Author(s):  
Linxiao Wei ◽  
Yijun Hu
Keyword(s):  
Standard Deviation ◽  
Portfolio Management ◽  
Risk Measures ◽  
Risk Measure ◽  
Axiomatic Approach ◽  
Capital Allocation ◽  
Axiom System ◽  
Multivariate Risk ◽  
Multivariate Risk Measures ◽  
Multivariate Risk Measure

AbstractCapital allocation is of central importance in portfolio management and risk-based performance measurement. Capital allocations for univariate risk measures have been extensively studied in the finance literature. In contrast to this situation, few papers dealt with capital allocations for multivariate risk measures. In this paper, we propose an axiom system for capital allocation with multivariate risk measures. We first recall the class of the positively homogeneous and subadditive multivariate risk measures, and provide the corresponding representation results. Then it is shown that for a given positively homogeneous and subadditive multivariate risk measure, there exists a capital allocation principle. Furthermore, the uniqueness of the capital allocation principe is characterized. Finally, examples are also given to derive the explicit capital allocation principles for the multivariate risk measures based on mean and standard deviation, including the multivariate mean-standard-deviation risk measures.

UNDERSTANDING THE VISCOELASTIC PROPERTIES OF RABBIT CORNEA BASED ON STRESS RELAXATION TESTS AND CYCLIC UNIAXIAL TESTS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740035 ◽  
Author(s):  
HAIXIA ZHANG ◽  
XIUQING QIAN ◽  
LIN LI ◽  
ZHICHENG LIU
Keyword(s):  
Stress Relaxation ◽  
Test Data ◽  
Viscoelastic Properties ◽  
Linear Optimization ◽  
Constitutive Models ◽  
Optimization Techniques ◽  
Rabbit Cornea ◽  
Uniaxial Test ◽  
Material Parameters ◽  
Biomechanical Behavior

Background: Determining the viscoelastic properties of cornea is important in the fields of understanding of the tissue’s response to mechanical actions and the accurate numerical simulation of corneal biomechanical behavior under the effects of keratoconus and refractive surgery. To address this need, we present an approach to model the viscoelastic response of rabbit cornea from uniaxial test data. Methods: The corneal strip samples from six rabbits were obtained to perform cyclic uniaxial tension tests and stress relaxation tests. We investigated the suitability of six constitutive models, including empirical models and hyperelastic models, by a quasi-linear viscoelastic law. Applying non-linear optimization techniques, we found material parameters for each different strip sample. Results and conclusions: The model gave a better fit to loading data with [Formula: see text], and predicted the unloading data in the cyclic uniaxial tests with errors-of-fit ranging from 0.03 to 0.06. The results indicate that the best model is the power of the first invariant of strain with Prony form relaxation model, and that the method to identify the material parameters are valid for modeling the visoelastic response of cornea from uniaxial test data.

scholarly journals The effect of measuring light photolysis on the kinetics of reaction of reduced cytochrome a3 and carbon monoxide

1978 ◽  
Vol 175 (3) ◽  
pp. 1137-1138 ◽  
Author(s):  
K de Fonseka ◽  
B Chance
Keyword(s):  
Experimental Data ◽  
Carbon Monoxide ◽  
Linear Optimization ◽  
Optimization Techniques ◽  
Non Linear ◽  
Kinetics Of Reaction ◽  
Kinetics Of ◽  
Intermediate Mechanism

The kinetics of reaction of reduced cytochrome a3 and CO are re-investigated by non-linear optimization techniques. When photolysis by the monitoring light is taken into account, the experimental data are best fitted by a single-intermediate mechanism.

scholarly journals Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

2015 ◽  
Vol 32 (03) ◽  
pp. 1550019 ◽  
Author(s):  
Jie Xu ◽  
Edward Huang ◽  
Chun-Hung Chen ◽  
Loo Hay Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
High Performance ◽  
Manufacturing Systems ◽  
Stochastic Systems ◽  
Simulation Optimization ◽  
Big Data Analytics ◽  
Optimization Techniques ◽  
New Era ◽  
Recent Developments

Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

Scenario optimization technique for the assessment of downside-risk and investable portfolios in post-financial crisis

2015 ◽  
Vol 02 (03) ◽  
pp. 1550028 ◽  
Author(s):  
Mazin A. M. Al Janabi
Keyword(s):  
Portfolio Management ◽  
Value At Risk ◽  
Management Practices ◽  
Optimization Technique ◽  
Downside Risk ◽  
Scenario Optimization ◽  
Risk Algorithm ◽  
Financial Portfolios ◽  
Risk Parameters ◽  
Risk Limits

The aim of this paper is to develop an optimization technique for the assessment of downside-risk limits and investable financial portfolios under crisis-driven outlooks subject to applying meaningful financial and operational constraints. The simulation and testing methods are based on the renowned concept of liquidity-adjusted value-at-risk (LVaR) along with the development of an optimization risk-algorithm utilizing matrix–algebra technique. With the purpose of demonstrating the effectiveness of LVaR and stress-testing techniques, real-world quantitative analysis of structured equity portfolios are depicted for the Gulf Cooperation Council (GCC) financial markets. To this end, several structural simulations studies are accomplished with the goal of establishing realistic financial modeling algorithm for the calculation of downside-risk parameters and to empirically assess portfolio managers' optimal and investable portfolios. The developed methodology and risk valuation algorithms can aid in advancing risk assessment and portfolio management practices in emerging markets, particularly in the wake of the most recent credit crunch and the subsequent financial turmoil.

DRAWDOWN MEASURE IN PORTFOLIO OPTIMIZATION

2005 ◽  
Vol 08 (01) ◽  
pp. 13-58 ◽  
Author(s):  
ALEXEI CHEKHLOV ◽  
STANISLAV URYASEV ◽  
MICHAEL ZABARANKIN
Keyword(s):  
At Risk ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Sample Path ◽  
Real Life ◽  
Optimization Techniques ◽  
Conditional Value At Risk ◽  
Excess Loss

A new one-parameter family of risk measures called Conditional Drawdown (CDD) has been proposed. These measures of risk are functionals of the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter α, in the case of a single sample path, drawdown functional is defined as the mean of the worst (1 - α) * 100% drawdowns. The CDD measure generalizes the notion of the drawdown functional to a multi-scenario case and can be considered as a generalization of deviation measure to a dynamic case. The CDD measure includes the Maximal Drawdown and Average Drawdown as its limiting cases. Mathematical properties of the CDD measure have been studied and efficient optimization techniques for CDD computation and solving asset-allocation problems with a CDD measure have been developed. The CDD family of risk functionals is similar to Conditional Value-at-Risk (CVaR), which is also called Mean Shortfall, Mean Excess Loss, or Tail Value-at-Risk. Some recommendations on how to select the optimal risk functionals for getting practically stable portfolios have been provided. A real-life asset-allocation problem has been solved using the proposed measures. For this particular example, the optimal portfolios for cases of Maximal Drawdown, Average Drawdown, and several intermediate cases between these two have been found.

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