scholarly journals Investment Risk Measurement Based on Quantiles and Expectiles

2018 ◽  
Vol 5 (338) ◽  
pp. 213-227
Author(s):  
Grażyna Trzpiot
Keyword(s):  
At Risk ◽  
Quantile Regression ◽  
Value At Risk ◽  
Risk Measures ◽  
Quantile Function ◽  
Risk Measurement ◽  
Conditional Value At Risk ◽  
Investment Risk ◽  
Response Distribution ◽  
Expectile Regression

In the presented research, we attempt to examine special investment risk measurement. We use quantile regression as a model by describing more general properties of the response distribution. In quantile regression, we assume regression effects on the conditional quantile function of the response. In regression modelling, the focus is on extending linear regression (OLS), and in this paper we seek to apply expectile regression. The purpose of using both approaches is investment risk measurement. Both regression models are a version of least weighted squares model. The families of risk measures most commonly used in practice are the Value‑at‑Risk (VaR) and the Conditional Value‑at‑Risk (CVaR), which can be estimated by quantiles or expectiles in the tail of the response distribution.

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.

scholarly journals Market Risk Measurement

2020 ◽  
Author(s):  
Emese Lazar ◽  
Ning Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Preliminary Analysis ◽  
Risk Measures ◽  
Market Risk ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Regulatory Framework ◽  
Bank Capital ◽  
Bank Capital Requirements

This chapter presents a preliminary analysis on how some market risk measures dramatically increased during the COVID-19 pandemic, with measures computed over longer horizons experiencing more pronounced effects. We provide examples when regulatory market risk measurement proved to be suboptimal, overestimating risk. A further issue was the large number of Value-at-Risk ‘exceptions’ during the first few months of the crisis, which normally leads to overinflated bank capital requirements. The current regulatory framework should address these problems by suggesting improvements to the calculation of risk measures and/or by modifying the rules which determine capital requirements to make them appropriate and realistic in crisis situations.

scholarly journals Estimating Health Care Costs among Fragile and Conflict Affected States: An Elastic Net-Risk Measures Approach

2018 ◽  
Vol 7 (3) ◽  
pp. 175
Author(s):  
Kevin Wunderlich ◽  
Emmanuel Thompson
Keyword(s):  
Health Care ◽  
At Risk ◽  
Health Care Costs ◽  
Value At Risk ◽  
Risk Measures ◽  
Elastic Net ◽  
Conditional Value At Risk ◽  
Multiple Variables ◽  
Conflict Affected States ◽  
Care Costs

<span>Fragile and conflict affected states (FCAS) are those in which the government lacks the political will and/or capacity to provide the basic functions necessary for poverty reduction, economic development, and the security of human rights of their populations.</span><span>Until recent history, unfortunately, the majority of research conducted and universal health care debates have been centered around middle income and emerging economies. As a result, FCAS have been neglected from many global discussions and decisions. Due to this neglect, many FCAS do not have proper vaccinations and antibiotics. Seemingly, well estimated health care costs are a necessary stepping stone in improving the health of citizens among FCAS. Fortunately, developments in statistical learning theory combined with data obtained by the WBG and Transparency International make it possible to accurately model health care cost among FCAS. The data used in this paper consisted of 35 countries and 89 variables. Of these 89 variables, health care expenditure (HCE) was the only response variable. With 88 predictor variables, there was expected to be multicollinearity, which occurs when multiple variables share relatively large absolute correlation. Since multicollinearity is expected and the number of variables is far greater than the number of observations, this paper adopts Zou and Hastie’</span><span lang="IN">s </span><span>method of regularization via elastic net (ENET). In order to accurately estimate the maximum and expected maximum HCE among FCAS, well-known risk measures, such as Value at Risk and Conditional Value at Risk, and related quantities were obtained via Monte Carlo simulations. This paper obtained risk measures at 95 security level.</span>

Assessing the Efficiency of Investment Fund Management Using Quantile Risk Measures

2016 ◽  
Vol 11 (3) ◽  
pp. 277-298
Author(s):  
Anna Rutkowska-Ziarko ◽  
Przemysław Garsztka
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Alternative Methods ◽  
Rate Of Return ◽  
Conditional Value At Risk ◽  
Market Condition ◽  
Investment Fund ◽  
Fund Management ◽  
Investment Funds

The aim of the research is to compare the efficiency of managing selected Polish investment funds in various phases of stock market condition. The Value at Risk (VaR) and Conditional Value at Risk (CVaR) is used to construct efficiency ratios of fund management. Those funds investing in financial instruments have the most stable expected rate of return and the lowest risk, in all the analysed periods which made them highly effective. The article also discusses the alternative methods to VaR and CVaR estimation which are used in the study. It is noted VaR and CVaR estimates obtained using backtesting and using APARCH models give similar results.

scholarly journals Measurement of Commodity Price Risk: an overview of Brazilian agricultural markets

2017 ◽  
Vol 55 (3) ◽  
pp. 515-532
Author(s):  
Daniel Henrique Dario Capitani ◽  
Fabio Mattos
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Commodity Price ◽  
Downside Risk ◽  
Conditional Value At Risk ◽  
Price Risk ◽  
Agricultural Markets ◽  
Price Variability ◽  
Agricultural Industry

Abstract: This study explores different procedures to estimate price risk in commodity markets. Focusing on Brazilian agricultural markets, the paper proposes to assess both dispersion and downside risk measures using five different approaches (volatility, coefficient of variation, lower partial moments, value at risk and conditional value at risk). Results suggest that some commodities have large price variability but small downside risk, while other commodities show small price variability and large downside risk. Thus, there is no single answer to the question of which commodity exhibits more price risk, but rather distinct answers depending on how risk is perceived by different individuals. These findings are relevant for agents in the agricultural industry as they affect marketing and risk management decisions and for policy makers involved in support programs to agriculture.

scholarly journals Risk Analysis through the Half-Normal Distribution

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2080
Author(s):  
Maria-Teresa Bosch-Badia ◽  
Joan Montllor-Serrats ◽  
Maria-Antonia Tarrazon-Rodon
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Normal Distribution ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Risk Indicators ◽  
Conditional Value At Risk ◽  
Risk Matrices

We study the applicability of the half-normal distribution to the probability–severity risk analysis traditionally performed through risk matrices and continuous probability–consequence diagrams (CPCDs). To this end, we develop a model that adapts the financial risk measures Value-at-Risk (VaR) and Conditional Value at Risk (CVaR) to risky scenarios that face only negative impacts. This model leads to three risk indicators: The Hazards Index-at-Risk (HIaR), the Expected Hazards Damage (EHD), and the Conditional HIaR (CHIaR). HIaR measures the expected highest hazards impact under a certain probability, while EHD consists of the expected impact that stems from truncating the half-normal distribution at the HIaR point. CHIaR, in turn, measures the expected damage in the case it exceeds the HIaR. Therefore, the Truncated Risk Model that we develop generates a measure for hazards expectations (EHD) and another measure for hazards surprises (CHIaR). Our analysis includes deduction of the mathematical functions that relate HIaR, EHD, and CHIaR to one another as well as the expected loss estimated by risk matrices. By extending the model to the generalised half-normal distribution, we incorporate a shape parameter into the model that can be interpreted as a hazard aversion coefficient.

Sign in / Sign up

Export Citation Format

Share Document