The sum of two independent polynomially-modified hyperbolic secant random variables with application in computational finance

2021 ◽  
pp. 2150030
Author(s):  
A. A. L. Zadeh ◽  
Hojatollah Zakerzadeh ◽  
Hamzeh Torabi
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Hermite Polynomial ◽  
Risk Measures ◽  
Random Variables ◽  
Expected Shortfall ◽  
Computational Finance ◽  
Sample Period ◽  
Proposed Model ◽  
Multiple Assets

In this paper, by reshaping the hyperbolic secant distribution using Hermite polynomial, we devise a polynomially-modified hyperbolic secant distribution which is more flexible than secant distribution to capture the skewness, heavy-tailedness and kurtosis of data. As a portfolio possibly consists of multiple assets, the distribution of the sum of independent polynomially-modified hyperbolic secant random variables is derived. In exceptional cases, we evaluate risk measures such as value at risk and expected shortfall (ES) for the sum of two independent polynomially-modified hyperbolic secant random variables. Finally, using real datasets from four international computers stocks, such as Adobe Systems, Microsoft, Nvidia and Symantec Corporations, the effectiveness of the proposed model is shown by the goodness of Gram–Charlier-like expansion of hyperbolic secant law, for performance of value at risk and ES estimation, both in and out of the sample period.

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.

A LIQUIDATION RISK ADJUSTMENT FOR VALUE AT RISK AND EXPECTED SHORTFALL

2018 ◽  
Vol 21 (03) ◽  
pp. 1850010 ◽  
Author(s):  
LAKSHITHE WAGALATH ◽  
JORGE P. ZUBELLI
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Risk Adjustment ◽  
Value At Risk ◽  
Financial Institution ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Adjusted Risk ◽  
Risk Managers

This paper proposes an intuitive and flexible framework to quantify liquidation risk for financial institutions. We develop a model where the “fundamental” dynamics of assets is modified by price impacts from fund liquidations. We characterize mathematically the liquidation schedule of financial institutions and study in detail the fire sales resulting endogenously from margin constraints when a financial institution trades through an exchange. Our study enables to obtain tractable formulas for the value at risk and expected shortfall of a financial institution in the presence of fund liquidation. In particular, we find an additive decomposition for liquidation-adjusted risk measures. We show that such a measure can be expressed as a “fundamental” risk measure plus a liquidation risk adjustment that is proportional to the size of fund positions as a fraction of asset market depths. Our results can be used by risk managers in financial institutions to tackle liquidity events arising from fund liquidations better and adjust their portfolio allocations to liquidation risk more accurately.

scholarly journals Testes para avaliação das previsões de value-at-risk e expected shortfall

2019 ◽  
Vol 17 (4) ◽  
pp. 56
Author(s):  
Jaime Enrique Lincovil ◽  
Chang Chiann
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Pareto Distribution ◽  
Risk Measures ◽  
Generalized Pareto Distribution ◽  
Expected Shortfall ◽  
Empirical Power ◽  
Test Procedures ◽  
Generalized Pareto ◽  
Evaluating Forecasts

<p>Evaluating forecasts of risk measures, such as value–at–risk (VaR) and expected shortfall (ES), is an important process for financial institutions. Backtesting procedures were introduced to assess the efficiency of these forecasts. In this paper, we compare the empirical power of new classes of backtesting, for VaR and ES, from the statistical literature. Further, we employ these procedures to evaluate the efficiency of the forecasts generated by both the Historical Simulation method and two methods based on the Generalized Pareto Distribution. To evaluate VaR forecasts, the empirical power of the Geometric–VaR class of backtesting was, in general, higher than that of other tests in the simulated scenarios. This supports the advantages of using defined time periods and covariates in the test procedures. On the other hand, to evaluate ES forecasts, backtesting methods based on the conditional distribution of returns to the VaR performed well with large sample sizes. Additionally, we show that the method based on the generalized Pareto distribution using durations and covariates has optimal performance in forecasts of VaR and ES, according to backtesting.</p>

scholarly journals An evaluation and comparison of Value at Risk and Expected Shortfall

2018 ◽  
Vol 15 (4) ◽  
pp. 17-34 ◽  
Author(s):  
Tom Burdorf ◽  
Gary van Vuuren
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Emerging Economies ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Significance Level ◽  
Tail Conditional Expectation ◽  
The Uk

As a risk measure, Value at Risk (VaR) is neither sub-additive nor coherent. These drawbacks have coerced regulatory authorities to introduce and mandate Expected Shortfall (ES) as a mainstream regulatory risk management metric. VaR is, however, still needed to estimate the tail conditional expectation (the ES): the average of losses that are greater than the VaR at a significance level These two risk measures behave quite differently during growth and recession periods in developed and emerging economies. Using equity portfolios assembled from securities of the banking and retail sectors in the UK and South Africa, historical, variance-covariance and Monte Carlo approaches are used to determine VaR (and hence ES). The results are back-tested and compared, and normality assumptions are tested. Key findings are that the results of the variance covariance and the Monte Carlo approach are more consistent in all environments in comparison to the historical outcomes regardless of the equity portfolio regarded. The industries and periods analysed influenced the accuracy of the risk measures; the different economies did not.

scholarly journals Good-Deal Bounds for Option Prices under Value-at-Risk and Expected Shortfall Constraints

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 114
Author(s):  
Sascha Desmettre ◽  
Christian Laudagé ◽  
Jörn Sass
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Good Deal ◽  
Expected Shortfall ◽  
Option Prices ◽  
Strong Connection ◽  
Pricing Bounds ◽  
The Common ◽  
Rate Of Returns

In this paper, we deal with the pricing of European options in an incomplete market. We use the common risk measures Value-at-Risk and Expected Shortfall to define good-deals on a financial market with log-normally distributed rate of returns. We show that the pricing bounds obtained from the Value-at-Risk admit a non-smooth behavior under parameter changes. Additionally, we find situations in which the seller’s bound for a call option is smaller than the buyer’s bound. We identify the missing convexity of the Value-at-Risk as main reason for this behavior. Due to the strong connection between good-deal bounds and the theory of risk measures, we further obtain new insights in the finiteness and the continuity of risk measures based on multiple eligible assets in our setting.

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 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 Extreme Risk, Value-At-Risk And Expected Shortfall In The Gold Market

2014 ◽  
Vol 14 (1) ◽  
pp. 107
Author(s):  
Knowledge Chinhamu ◽  
Chun-Kai Huang ◽  
Chun-Sung Huang ◽  
Delson Chikobvu
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Effective Means ◽  
Value Theory ◽  
Expected Shortfall ◽  
Extreme Stress ◽  
Extreme Risk ◽  
Gold Market ◽  
The Impact

Extreme value theory (EVT) has been widely applied in fields such as hydrology and insurance. It is a tool used to reflect on probabilities associated with extreme, and thus rare, events. EVT is useful in modeling the impact of crashes or situations of extreme stress on investor portfolios. It describes the behavior of maxima or minima in a time series, i.e., tails of a distribution. In this paper, we propose the use of generalised Pareto distribution (GPD) to model extreme returns in the gold market. This method provides effective means of estimating tail risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). This is confirmed by various backtesting procedures. In particular, we utilize the Kupiec unconditional coverage test and the Christoffersen conditional coverage test for VaR backtesting, while the Bootstrap test is used for ES backtesting. The results indicate that GPD is superior to the traditional Gaussian and Students t models for VaR and ES estimations.

scholarly journals Seven Proofs for the Subadditivity of Expected Shortfall

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Paul Embrechts ◽  
Ruodu Wang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Expected Shortfall ◽  
Convex Order ◽  
Dual Representation ◽  
General Guideline

AbstractSubadditivity is the key property which distinguishes the popular risk measures Value-at-Risk and Expected Shortfall (ES). In this paper we offer seven proofs of the subadditivity of ES, some found in the literature and some not. One of the main objectives of this paper is to provide a general guideline for instructors to teach the subadditivity of ES in a course. We discuss the merits and suggest appropriate contexts for each proof.With different proofs, different important properties of ES are revealed, such as its dual representation, optimization properties, continuity, consistency with convex order, and natural estimators.

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).

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