Apports de la Théorie des Valeurs Extrêmes au calcul de la Value-at-Risk (Contributions of the Extreme Values Theory in the calculation of Value-at-Risk)

2018 ◽  
Author(s):  
Yassine Bakkar
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Extreme Values Theory

Value at Risk and Extreme Values

1998 ◽  
Vol 31 (16) ◽  
pp. 45-49
Author(s):  
François M. Longia
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values

scholarly journals STL Decomposition and SARIMA Model: The Case for Estimating Value-at-Risk of Covid-19 Increment Rate in DKI Jakarta

2021 ◽  
Vol 7 (2) ◽  
pp. 1-10
Author(s):  
Agnes Zahrani ◽  
Aniq A. Rohmawati ◽  
Siti Sa’adah
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Moving Average ◽  
Extreme Value ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Increment Rate ◽  
The Difference ◽  
Parameter Estimators

In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.

scholarly journals PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017)

2018 ◽  
Vol 7 (2) ◽  
pp. 212-223
Author(s):  
Ria Epelina Situmorang ◽  
Di Asih I Maruddani ◽  
Rukun Santoso
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Heavy Tail ◽  
Extreme Value ◽  
Return Rate ◽  
Optimal Portfolio ◽  
Average Risk ◽  
Generalized Extreme Value ◽  
Financial Investment

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

scholarly journals GENERALIZED PARETO DISTRIBUTION UNTUK PENGUKURAN VALUE AT RISK PADA PORTOFOLIO SAHAM SYARIAH DAN APLIKASINYA MENGGUNAKAN GUI MATLAB

2018 ◽  
Vol 7 (3) ◽  
pp. 224-235
Author(s):  
Desi Nur Rahma ◽  
Di Asih I Maruddani ◽  
Tarno Tarno
Keyword(s):  
At Risk ◽  
Capital Market ◽  
Value At Risk ◽  
Pareto Distribution ◽  
Extreme Values ◽  
Generalized Pareto Distribution ◽  
Generalized Pareto ◽  
Heavy Tailed ◽  
Is Value

The capital market is one of long-term investment alternative. One of the traded products is stock, including sharia stock. The risk measurement is an important thing for investor in other that can decrease investment loss. One of the popular methods now is Value at Risk (VaR). There are many financial data that have heavy tailed, because of extreme values, so Value at Risk Generalized Pareto Distribution is used for this case. This research also result a Matlab GUI programming application that can help users to measure the VaR. The purpose of this research is to analyze VaR with GPD approach with GUI Matlab for helping the computation in sharia stock. The data that is used in this case are PT XL Axiata Tbk, PT Waskita Karya (Persero) Tbk, dan PT Charoen Pokphand Indonesia Tbk on January, 2nd 2017 until May, 31st 2017. The results of VaRGPD are: EXCL single stock VaR 8,76% of investment, WSKT single stock VaR 4% of investment, CPIN single stock VaR 5,86% of investment, 2 assets portfolio (EXCL and WSKT) 4,09% of investment, 2 assets portfolio (EXCL and CPIN) 5,28% of investment, 2 assets portfolio (WSKT and CPIN) 3,68% of investment, and 3 assets portfolio (EXCL, WSKT, and CPIN) 3,75% of investment. It can be concluded that the portfolios more and more, the risk is smaller. It is because the possibility of all stocks of the company dropped together is small. Keywords: Generalized Pareto Distribution, Value at Risk, Graphical User Interface, sharia stock

A risk model to compute the volatility and the need for collateral margins in energy futures contracts in Brazil

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pedro Argento ◽  
Marcelo Cabus Klotzle ◽  
Antonio Carlos Figueiredo Pinto ◽  
Leonardo Lima Gomes
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Extreme Values ◽  
Safety Margin ◽  
Conditional Value At Risk ◽  
Futures Contracts ◽  
Content Type ◽  
Energy Futures ◽  
Risk Dimensions

Purpose Brazil is characterized by the inexistence of a more robust system of guarantees and rules to minimize risks and protect agents in energy futures contracts. In this sense, this study aims to answer the question of how a centralized clearing agent can compute safety margin requirements to help reduce the systemic risk of the energy futures contracts market in Brazil. Design/methodology/approach The intermediate steps and specific objectives are to analyze the volatility behavior, identify the autoregressive conditional heteroscedasticity effects and model the variance of the return series. Based on this, the authors calculate the value-at-risk and conditional value-at-risk metrics for the energy futures contracts. As a robustness test, the authors added a peak over threshold methodology from extreme values theory. Findings In general, monthly products require margins because of their higher variance. With the asymmetrical distribution of returns, the authors needed to consider different maintenance margins for the long and short positions. It was also shown that two guarantee margins were required to secure the contracts as follows: the initial margin and the maintenance margin. The three factors that defined the size of the maintenance margin the volatility, skewness and kurtosis of the return series. Originality/value The contribution of this study lies in promoting the understanding of the risk dimensions of the energy derivatives market in Brazil and it offers concrete recommendations for how to mitigate this risk through market mechanisms and structures. Similar arrangements can be applied to other emerging markets.

scholarly journals Using Entropy to Forecast Bitcoin’s Daily Conditional Value at Risk

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 7
Author(s):  
Hellinton H. Takada ◽  
Sylvio X. Azevedo ◽  
Julio M. Stern ◽  
Celma O. Ribeiro
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Time Interval ◽  
Investment Portfolio ◽  
Investment Portfolio Optimization ◽  
Symbolic Time Series Analysis ◽  
Better Than

Conditional value at risk (CVaR), or expected shortfall, is a risk measure for investments according to Rockafellar and Uryasev. Yamai and Yoshiba define CVaR as the conditional expectation of loss given that the loss is beyond the value at risk (VaR) level. The VaR is a risk measure that represents how much an investment might lose during usual market conditions with a given probability in a time interval. In particular, Rockafellar and Uryasev show that CVaR is superior to VaR in applications related to investment portfolio optimization. On the other hand, the Shannon entropy has been used as an uncertainty measure in investments and, in particular, to forecast the Bitcoin’s daily VaR. In this paper, we estimate the entropy of intraday distribution of Bitcoin’s logreturns through the symbolic time series analysis (STSA) and we forecast Bitcoin’s daily CVaR using the estimated entropy. We find that the entropy is positively correlated to the likelihood of extreme values of Bitcoin’s daily logreturns using a logistic regression model based on CVaR and the use of entropy to forecast the Bitcoin’s daily CVaR of the next day performs better than the naive use of the historical CVaR.

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