scholarly journals Computing value-at-risk and expected shortfall in operational risk

2021 ◽  
Author(s):  
Desire Issiaka Bakassa-Traore
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Operational Risk ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Basel Committee ◽  
Loss Distribution Approach ◽  
Capital Charge ◽  
Heavy Tailed

Operational Risk has become more popular in the past fifteen years. The Basel committee realized its importance and banks have to allocate more capital charge, yet this is still not enough. With these new rules, banks have put in place new procedures to compute their risk measures and allocate enough capital charge to avoid bankruptcy. The Basel committee under Basel II has proposed different approaches to compute risk measures for Operational Risk, namely the Basic Indicator Approach, the Advanced Measurement Approach and the Standardized Approach. In our research, we will study the case of Loss Distribution Approach, which has been discussed before, and will contribute to the field by using a heavy-tailed distributed severity: g-and-h distributed. Then, we will analyze and test some methods to compute the value-at-risk( VaR) and conditional value-at-risk or expected shortfall (CVaR).

scholarly journals Computing value-at-risk and expected shortfall in operational risk

2021 ◽  
Author(s):  
Desire Issiaka Bakassa-Traore
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Operational Risk ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Basel Committee ◽  
Loss Distribution Approach ◽  
Capital Charge ◽  
Heavy Tailed

Operational Risk has become more popular in the past fifteen years. The Basel committee realized its importance and banks have to allocate more capital charge, yet this is still not enough. With these new rules, banks have put in place new procedures to compute their risk measures and allocate enough capital charge to avoid bankruptcy. The Basel committee under Basel II has proposed different approaches to compute risk measures for Operational Risk, namely the Basic Indicator Approach, the Advanced Measurement Approach and the Standardized Approach. In our research, we will study the case of Loss Distribution Approach, which has been discussed before, and will contribute to the field by using a heavy-tailed distributed severity: g-and-h distributed. Then, we will analyze and test some methods to compute the value-at-risk( VaR) and conditional value-at-risk or expected shortfall (CVaR).

scholarly journals Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures

2020 ◽  
Author(s):  
Denisa Banulescu-Radu ◽  
Christophe Hurlin ◽  
Jérémy Leymarie ◽  
Olivier Scaillet
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Finite Sample ◽  
Marginal Expected Shortfall

This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as the systemic expected shortfall (SES), the systemic risk measure (SRISK), or the delta conditional value-at-risk ([Formula: see text]CoVaR). We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application to a panel of U.S. financial institutions is conducted to assess the validity of MES, SRISK, and [Formula: see text]CoVaR forecasts issued from a bivariate GARCH model with a dynamic conditional correlation structure. Our results show that this model provides valid forecasts for MES and SRISK when considering a medium-term horizon. Finally, we propose an early warning system indicator for future systemic crises deduced from these backtests. Our indicator quantifies how much is the measurement error issued by a systemic risk forecast at a given point in time which can serve for the early detection of global market reversals. This paper was accepted by Kay Giesecke, finance.

scholarly journals On the existence of an optimal estimation window for risk measures

2015 ◽  
Vol 4 (1and2) ◽  
pp. 28
Author(s):  
Marcelo Brutti Righi ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Optimal Estimation ◽  
Expected Shortfall ◽  
Estimation Bias ◽  
Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

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 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.

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