Assessing Optimal Retention With Quantile and Expectile Risk Measure

The present study extends the portfolio evaluation framework provided by Sharpe (1964) and Treynor (1965) by including the parameter of market timing with the help of a non-parametric framework. Data envelopment analysis has been used in the present exercise to evaluate the performance 79 mutual funds schemes operating in India for three different phases using two different models. Estimation of technical efficiency on the basis of both the models suggests that period 2 performance is substantially divergent from period 1 and 3. Also, higher moments framework gives a better measure of performance as it accounts not only the standard risk measure but also for skewness and kurtosis characteristics of returns.

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Risk Management Optimization for Sovereign Debt Restructuring

Journal of Globalization and Development ◽

10.1515/jgd-2015-0015 ◽

2015 ◽

Vol 6 (2) ◽

Cited By ~ 3

Author(s):

Andrea Consiglio ◽

Stavros A. Zenios

Keyword(s):

Risk Management ◽

Scenario Analysis ◽

Sovereign Debt ◽

Risk Measure ◽

Policy Tools ◽

Debt Restructuring ◽

Tail Risk ◽

Debt Sustainability ◽

Scenario Optimization ◽

Management Optimization

AbstractDebt restructuring is one of the policy tools available for resolving sovereign debt crises and, while unorthodox, it is not uncommon. We propose a scenario analysis for debt sustainability and integrate it with scenario optimization for risk management in restructuring sovereign debt. The scenario dynamics of debt-to-GDP ratio are used to define a tail risk measure, termed

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Immunization Strategies for Funding Multiple Inflation-Linked Retirement Income Benefits

Risks ◽

10.3390/risks9040060 ◽

2021 ◽

Vol 9 (4) ◽

pp. 60

Author(s):

Cláudia Simões ◽

Luís Oliveira ◽

Jorge M. Bravo

Keyword(s):

Interest Rate ◽

Yield Curve ◽

Risk Measure ◽

Management Strategies ◽

Interest Rate Risk ◽

Insurance Companies ◽

Retirement Income ◽

Investment Strategies ◽

Immunization Strategies ◽

The Impact

Protecting against unexpected yield curve, inflation, and longevity shifts are some of the most critical issues institutional and private investors must solve when managing post-retirement income benefits. This paper empirically investigates the performance of alternative immunization strategies for funding targeted multiple liabilities that are fixed in timing but random in size (inflation-linked), i.e., that change stochastically according to consumer price or wage level indexes. The immunization procedure is based on a targeted minimax strategy considering the M-Absolute as the interest rate risk measure. We investigate to what extent the inflation-hedging properties of ILBs in asset liability management strategies targeted to immunize multiple liabilities of random size are superior to that of nominal bonds. We use two alternative datasets comprising daily closing prices for U.S. Treasuries and U.S. inflation-linked bonds from 2000 to 2018. The immunization performance is tested over 3-year and 5-year investment horizons, uses real and not simulated bond data and takes into consideration the impact of transaction costs in the performance of immunization strategies and in the selection of optimal investment strategies. The results show that the multiple liability immunization strategy using inflation-linked bonds outperforms the equivalent strategy using nominal bonds and is robust even in a nearly zero interest rate scenario. These results have important implications in the design and structuring of ALM liability-driven investment strategies, particularly for retirement income providers such as pension schemes or life insurance companies.

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Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Mathematics ◽

10.3390/math9060692 ◽

2021 ◽

Vol 9 (6) ◽

pp. 692

Author(s):

Clara Calvo ◽

Carlos Ivorra ◽

Vicente Liern ◽

Blanca Pérez-Gladish

Keyword(s):

Risk Measure ◽

Statistical Information ◽

Expected Returns ◽

Financial Assets ◽

Expected Return ◽

Additional Information ◽

Subjective Risk ◽

The Impact ◽

Statistical Risk ◽

Reduction Index

Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

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Modified Mean-Variance Risk Measures for Long-Term Portfolios

Mathematics ◽

10.3390/math9020111 ◽

2021 ◽

Vol 9 (2) ◽

pp. 111

Author(s):

Hyungbin Park

Keyword(s):

Risk Measures ◽

Risk Measure ◽

Investment Strategy ◽

Stochastic Volatility Model ◽

Heston Model ◽

Volatility Model ◽

Variance Risk ◽

Investment Portfolios ◽

Mean Variance

This paper proposes modified mean-variance risk measures for long-term investment portfolios. Two types of portfolios are considered: constant proportion portfolios and increasing amount portfolios. They are widely used in finance for investing assets and developing derivative securities. We compare the long-term behavior of a conventional mean-variance risk measure and a modified one of the two types of portfolios, and we discuss the benefits of the modified measure. Subsequently, an optimal long-term investment strategy is derived. We show that the modified risk measure reflects the investor’s risk aversion on the optimal long-term investment strategy; however, the conventional one does not. Several factor models are discussed as concrete examples: the Black–Scholes model, Kim–Omberg model, Heston model, and 3/2 stochastic volatility model.

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The Effect of HbA1c Variability as a Risk Measure for Microangiopathy in Type 1 Diabetes Mellitus

Diagnostics ◽

10.3390/diagnostics11071151 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1151

Author(s):

Pedro Romero-Aroca ◽

Raul Navarro-Gil ◽

Albert Feliu ◽

Aida Valls ◽

Antonio Moreno ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Diabetic Retinopathy ◽

Type 1 Diabetes Mellitus ◽

Risk Measure ◽

Case Series ◽

Multivariate Statistical ◽

Case Series Study ◽

Prospective Case Series

Background: To measure the relationship between variability in HbA1c and microalbuminuria (MA) and diabetic retinopathy (DR) in the long term. Methods: A prospective case-series study, was conducted on 366 Type 1 Diabetes Mellitus patients with normoalbuminuria and without diabetic retinopathy at inclusion. The cohort was followed for a period of 12 years. The Cox survival analysis was used for the multivariate statistical study. The effect of variability in microangiopathy (retinopathy and nephropathy) was evaluated by calculating the standard deviation of HbA1c (SD-HbA1c), the coefficient of variation of HbA1c (CV-HbA1c), average real variability (ARV-HbA1c) and variability irrespective of the mean (VIM-HbA1c) adjusted for the other known variables. Results: A total of 106 patients developed diabetic retinopathy (29%) and 73 microalbuminuria (19.9%). Overt diabetic nephropathy, by our definition, affected only five patients (1.36%). Statistical results show that the current age, mean HbA1c, SD-HbA1c and ARV-HbA1c are significant in the development of diabetic retinopathy. Microalbuminuria was significant for current age, mean HbA1c, CV-HbA1c and ARV-HbA1c. Conclusions: By measuring the variability in HbA1c, we can use SD-HbA1c and ARV-HbA1c as possible targets for judging which patients are at risk of developing DR and MA, and CV-HbA1c as the target for severe DR.

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OPTIMAL REINSURANCE FROM THE VIEWPOINTS OF BOTH AN INSURER AND A REINSURER UNDER THE CVAR RISK MEASURE AND VAJDA CONDITION

Astin Bulletin ◽

10.1017/asb.2021.9 ◽

2021 ◽

pp. 1-29

Author(s):

Yanhong Chen

Keyword(s):

Loss Function ◽

Value At Risk ◽

Numerical Study ◽

Risk Measure ◽

Convex Combination ◽

Weighting Factor ◽

Loss Functions ◽

Conditional Value At Risk ◽

Optimal Reinsurance ◽

The Impact

ABSTRACT In this paper, we study the optimal reinsurance contracts that minimize the convex combination of the Conditional Value-at-Risk (CVaR) of the insurer’s loss and the reinsurer’s loss over the class of ceded loss functions such that the retained loss function is increasing and the ceded loss function satisfies Vajda condition. Among a general class of reinsurance premium principles that satisfy the properties of risk loading and convex order preserving, the optimal solutions are obtained. Our results show that the optimal ceded loss functions are in the form of five interconnected segments for general reinsurance premium principles, and they can be further simplified to four interconnected segments if more properties are added to reinsurance premium principles. Finally, we derive optimal parameters for the expected value premium principle and give a numerical study to analyze the impact of the weighting factor on the optimal reinsurance.

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