scholarly journals Multiple-step value-at-risk forecasts based on volatility-filtered MIDAS quantile regression: Evidence from major investment assets

2021 ◽  
Vol 18 (3) ◽  
pp. 372-384
Author(s):  
Qian Chen ◽  
Xiang Gao ◽  
Xiaoxuan Huang ◽  
Xi Li
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Model Misspecification ◽  
Low Frequency ◽  
Tail Dependence ◽  
Risk Level ◽  
Proposed Model ◽  
Q Model ◽  
Global Stock Markets ◽  
Multiple Step

Forecasting multiple-step value-at-risk (VaR) consistently across asset classes is hindered by the limited sample size of low-frequency returns and the potential model misspecification when assuming identical return distributions over different holding periods. This paper hence investigates the predictive power for multi-step VaR of a framework that models separately the volatility component and the error term of the return distribution. The proposed model is illustrated with ten asset returns series including global stock markets, commodity futures, and currency exchange products. The estimation results confirm that the volatility-filter residuals demonstrate distinguished tail dynamics to that of the return series. The estimation results suggest that volatility-filtered residuals may have either negative or positive tail dependence, unlike the unanimous negative tail dependence in the return series. By comparing the proposed model to several alternative approaches, the results from both the formal and informal tests show that the specification under concern performs equivalently well if not better than its top competitors at the 2.5% and 5% risk level in terms of accuracy and validity. The proposed model also generates more consistent VaR forecasts under both the 5-step and 10-step setup than the MIDAS-Q model. AcknowledgmentThe authors are grateful to the editor and an anonymous referee. This research is sponsored by the National Natural Science Foundation of China (Award Number: 71501117). All remaining errors are our own.

Developing an Algorithm for Urban Flood Management With the Aim of Reducing Damage and Costs Using the Concept of Conditional Value at Risk 

2021 ◽  
Author(s):  
Pedram Eshaghieh Firoozabadi ◽  
sara nazif ◽  
Seyed Abbas Hosseini ◽  
Jafar Yazdi
Keyword(s):  
At Risk ◽  
Hybrid Model ◽  
Best Management Practices ◽  
Value At Risk ◽  
Management Practices ◽  
Flood Management ◽  
Surface Flow ◽  
Conditional Value At Risk ◽  
Urban Flood ◽  
Proposed Model

Abstract Flooding in urban area affects the lives of people and could cause huge damages. In this study, a model is proposed for urban flood management with the aim of reducing the total costs. For this purpose, a hybrid model has been developed using SWMM and a quasi-two-dimensional model based on the cellular automata (CA) capable of considering surface flow infiltration. Based on the hybrid model outputs, the best management practices (BMPs) scenarios are proposed. In the next step, a damage estimation model has been developed using depth-damage curves. The amount of damage has been estimated for the scenarios in different rainfall return periods to obtain the damage and cost- probability functions. The conditional value at risk (CVaR) are estimated based on these functions which is the basis of decision making about the scenarios. The proposed model is examined in an urban catchment located in Tehran, Iran. In this study, five scenarios have been designed on the basis of different BMPs. It has been found that the scenario of permeable pavements has the lowest risk. The proposed model enables the decision makers to choose the best scenario with the minimum cost taking into account the risk associated with each scenario.

scholarly journals On Conditional Value at Risk (CoVaR) for tail-dependent copulas

2017 ◽  
Vol 5 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Piotr Jaworski
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Tail Dependence ◽  
Extreme Value ◽  
Conditional Value At Risk ◽  
Dependence Function ◽  
Conditioning Event

Abstract The paper deals with Conditional Value at Risk (CoVaR) for copulas with nontrivial tail dependence. We show that both in the standard and the modified settings, the tail dependence function determines the limiting properties of CoVaR as the conditioning event becomes more extreme. The results are illustrated with examples using the extreme value, conic and truncation invariant families of bivariate tail-dependent copulas.

scholarly journals ESTIMASI NILAI CONDITIONAL VALUE AT RISK (CVaR) PORTOFOLIO MENGGUNAKAN METODE EVT-GJR-VINE COPULA

2019 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
NI WAYAN UCHI YUSHI ARI SUDINA ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Risk Level ◽  
Copula Method ◽  
Vine Copula

Conditional value at risk (CVaR) is widely used in risk measure that takes into account losses exceeding the value at risk level. The aim of this research is to compare the performance of the EVT-GJR-vine copula method and EVT-GARCH-vine copula method in estimating CVaR of the portfolio using backtesting. Based on the backtesting results, it was found that the EVT-GJR-vine copula method have better performance when compared to the EVT-GARCH-vine copula method in estimating the CVaR value of the portfolio. This can be seen from the statistical values ??, and  of EVT-GJR-vine copula method which is generally smaller than the statistical values , and of the EVT-GARCH-vine copula method.

scholarly journals Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates

2015 ◽  
Vol 54 ◽  
pp. 129-140 ◽  
Author(s):  
Karl Friedrich Siburg ◽  
Pavel Stoimenov ◽  
Gregor N.F. Weiß
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Tail Dependence ◽  
Lower Tail

scholarly journals ESTIMASI VALUE AT RISK PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH-COPULA (Studi Kasus : Harga Penutupan Saham Harian Unilever Indonesia dan Kimia Farma Periode 1 Januari 2013- 31 Desember 2016)

2018 ◽  
Vol 7 (4) ◽  
pp. 397-407
Author(s):  
Lingga Bayu Prasetya ◽  
Dwi Ispriyanti ◽  
Alan Prahutama
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Tail Dependence ◽  
Conditional Heteroscedasticity ◽  
Mutual Correlation ◽  
Copula Method ◽  
Stock Portfolio ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Value ◽  
Return Data

Any investment in the stock market will earn returns accompanied by risks. Return and risk has a mutual correlation that equilibrium. The formation of a portfolio is intended to provide a lower risk or with the same risk but provide a higher return. Value at Risk (VaR) is a instrument to analyze risk management. Time series model used in stock return data that it has not normal distribution and heteroscedastisicity is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH-Copula is a combined method of GARCH and Copula. The Copula method is used in joint distribution modeling because it does not require the assumption of normality of the data and can capture tail dependence between each variable. This research uses return data from stock closing prices of Unilever Indonesia and Kimia Farma period January 1, 2013 until December 31, 2016. Copula model is selected based on the highest likelihood log value is Copula Clayton. Value at Risk estimates of Unilever Indonesia and Kimia Farma's stock portfolio on the same weight were performed using Monte Carlo simulation with backtesting of 30 days period data at 95% confidence level. Keywords : Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Copula, Value at Risk

Wholesale Price for Supply Chain Coordination via Conditional Value-at-Risk Minimization

2010 ◽  
Vol 20-23 ◽  
pp. 88-93 ◽  
Author(s):  
Chuan Xu Wang
Keyword(s):  
At Risk ◽  
Supply Chain ◽  
Value At Risk ◽  
Supply Chain Coordination ◽  
Wholesale Price ◽  
Conditional Value At Risk ◽  
Optimal Order ◽  
Individual Member ◽  
Risk Minimization ◽  
Proposed Model

The theory of the conditional value-at-risk (CVaR) in financial risk management is considered in this paper to develop a model of supply chain coordination with a wholesale pricing policy. The proposed model solves the drawbacks of objective function in current supply chain coordination model. A numerical example is given to demonstrate the effectiveness of the proposed model. The following helpful conclusions are drawn from the paper: with the increase of the degree of risk averting for supply chain individual member, the optimal order quantity of supply chain is decreasing, while the optimal profit is decreasing; If supplier’s risk averting degree increases, supplier has to increase wholesale price to achieve supply chain coordination; If retailer’s risk averting degree increases, supplier has to decrease wholesale price to achieve supply chain coordination.

scholarly journals Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4147
Author(s):  
Krzysztof Echaust ◽  
Małgorzata Just
Keyword(s):  
At Risk ◽  
Crude Oil ◽  
Value At Risk ◽  
Tail Dependence ◽  
Oil Price ◽  
Health Crisis ◽  
Oil Market ◽  
Volatility Index ◽  
Conditional Copula ◽  
The Impact

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible.

scholarly journals Deposit Mudarabah Investment Risk Analysis Method Value at Risk (VaR)

2020 ◽  
Vol 4 (02) ◽  
Author(s):  
Iin Emy Prastiwi
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Statements ◽  
Risk Level ◽  
P Value ◽  
Investment Risk ◽  
Average Risk ◽  
Risk And Return ◽  
Net Return ◽  
Significant Probability

Abstract The purpose of this study is to understand the risk and return on net return on mudharabah deposits in Islamic banks using the Value at Risk (VaR) approach. The objects in this study are quarterly financial statements of Bank Syariah Mandiri, Bank BRI Syariah, and Muamalat Bank for three years, 2015-2017. The VaR analysis results show that the average risk of mudharabah deposit investment for 3 years in Bank Syariah Mandiri is 2015 at 6.61% and net return -0.53%, in 2016 the risk is 0.14% and net return 3.21 %, in 2017 the risk is 0.17% and net return is 0.32%. BRI Syariah Bank is 2015 at 0.08% and net return of 4.28%, in 2016 the risk is 0.07% and net return 3.77%, in 2017 the risk is 0.08% and net return 42.81% . and Bank Muamalat is 2015 at 0.63% and net return of 0.04%, in 2016 the risk is 0.40% and net return is 0.08%, in 2017 the risk is 0.14% and net return is 0.26%. In addition there are differences in the level of risk and return (net return) in Bank Syariah Mandiri, BRI Syariah, and Muamalat Bank with significant probability (p-value) for the risk level of 0.005 and return (net return) of 0.045. From the risk level and net return for three years, BRI Syariah Bank is a bank that has prospective value. Key Words : VaR, risk, net return, mudharabah deposit

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.

Risk Analysis of Portfolios Under Uncertainty: Minimizing Average Rates of Falling

2011 ◽  
Vol 15 (1) ◽  
pp. 56-62 ◽  
Author(s):  
Yuji Yoshida ◽  
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Asset Prices ◽  
Average Rate ◽  
Rate Of Return ◽  
Risk Of Falling ◽  
Average Value ◽  
Portfolio Problem ◽  
Proposed Model ◽  
Average Value At Risk

A portfolio model to minimize the risk of falling under uncertainty is discussed. The risk of falling is represented by the value-at-risk of rate of return. Introducing the perception-based extension of the average value-at-risk, this paper formulates a portfolio problem to minimize the risk of falling with fuzzy random variables. In the proposed model, randomness and fuzziness are evaluated respectively by the probabilistic expectation and the mean with evaluation weights and λ-mean functions. The analytical solutions of the portfolio problem regarding the risk of falling are given. This paper gives formulae to show the explicit relations among the following important parameters in portfolio: the expected rate of return, the risk probability of falling and bankruptcy, and the average rate of falling regarding the asset prices. A numerical example is given to explain how to obtain the optimal portfolio and these parameters from the asset prices in the stock market.

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