scholarly journals The Effect of Asymmetries on Stock Index Return Value‐at‐Risk Estimates

2003 ◽  
Vol 4 (2) ◽  
pp. 29-42 ◽  
Author(s):  
CHRIS BROOKS ◽  
GITA PERSAND
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Index ◽  
Risk Estimates ◽  
Return Value

scholarly journals How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 76
Author(s):  
Saswat Patra ◽  
Malay Bhattacharyya
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Risk Measure ◽  
Stock Index ◽  
Risk Exposure ◽  
Index Options ◽  
Significance Level ◽  
Significance Levels ◽  
Better Than

This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a portfolio. MaxVaR is a more stringent risk measure as it assesses the risk during the risk horizon. For a 30-day maturity option, we find that MaxVaR can be 40% higher than VaR at a 5% significance level. It highlights the importance of MaxVaR as a risk measure and shows that the risk is vastly underestimated when VaR is used as the measure for risk. The sensitivity of MaxVaR with respect to option characteristics like moneyness, time to maturity and risk horizons at different significance levels are observed. Further, interestingly enough we find that the MaxVar to VaR ratio is higher for stocks than the options and we can surmise that stock returns are more volatile than options. For robustness, the study is carried out under different distributional assumptions on residuals and for different stock index options.

scholarly journals Asymptotic Value-at-Risk Estimates for Sums of Dependent Random Variables

2003 ◽  
Vol 33 (1) ◽  
pp. 75-92 ◽  
Author(s):  
Mario V. Wüthrich
Keyword(s):  
At Risk ◽  
Asymptotic Behaviour ◽  
Value At Risk ◽  
Random Variables ◽  
Important Application ◽  
Archimedean Copulas ◽  
Dependent Random Variables ◽  
Asymptotic Value ◽  
Risk Estimates

We estimate Value-at-Risk for sums of dependent random variables. We model multivariate dependent random variables using archimedean copulas. This structure allows one to calculate the asymptotic behaviour of extremal events. An important application of such results are Value-at-Risk estimates for sums of dependent random variables.

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

scholarly journals Research on the Value at Risk of Basis for Stock Index Futures Hedging in China Based on Two-State Markov Process and Semiparametric RS-GARCH Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Liang Wang ◽  
Tingjia Xu ◽  
Longhao Qin ◽  
Chenge Liu
Keyword(s):  
At Risk ◽  
Markov Process ◽  
Value At Risk ◽  
Likelihood Function ◽  
Parametric Method ◽  
Garch Model ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Futures Hedging

This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS-GARCH model can effectively describe the state transition of variance in VaR and the two-state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS-GARCH models based on two-state Markov process. Furthermore, the logarithm likelihood function method and the kernel estimation with invariable bandwidth method are used for VaR estimation and empirical analysis. It is found that the three fitting errors (MSE, MAD, and QLIKE) of conditional variance calculated by semiparametric model are significantly smaller than that of the parametric model. The results of Kupiec backtesting on VaR obtained by the two models show that the failure days of the former are less than or equal to that of the latter, so it can be inferred that the semiparametric RS-GARCH model constructed in this paper is more effective in estimating the Value at Risk of the basis for Chinese stock index futures. In addition, the mean value and standard deviation of VaR obtained by the semiparametric RS-GARCH model are smaller than that of the parametric method, which can prove that the former model is more conservative in risk estimation.

Evaluating Value-at-Risk Estimates: A Cross-Section Approach

2007 ◽  
pp. 213-225
Author(s):  
Raffaele Zenti ◽  
Massimiliano Pallotta ◽  
Claudio Marsala
Keyword(s):  
At Risk ◽  
Cross Section ◽  
Value At Risk ◽  
Risk Estimates
Sign in / Sign up

Export Citation Format

Share Document