scholarly journals On Double Value at Risk

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 31 ◽  
Author(s):  
Wanbing Zhang ◽  
Sisi Zhang ◽  
Peibiao Zhao
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Confidence Region ◽  
Ratio Method ◽  
Two Dimensional ◽  
Income Risk ◽  
One Dimensional ◽  
Joint Confidence Region ◽  
The Empirical Analysis ◽  
Likelihood Ratio Method

Value at Risk (VaR) is used to illustrate the maximum potential loss under a given confidence level, and is just a single indicator to evaluate risk ignoring any information about income. The present paper will generalize one-dimensional VaR to two-dimensional VaR with income-risk double indicators. We first construct a double-VaR with ( μ , σ 2 ) (or ( μ , V a R 2 ) ) indicators, and deduce the joint confidence region of ( μ , σ 2 ) (or ( μ , V a R 2 ) ) by virtue of the two-dimensional likelihood ratio method. Finally, an example to cover the empirical analysis of two double-VaR models is stated.

scholarly journals Market Risk Analysis of Energy in Vietnam

Risks ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 112 ◽  
Author(s):  
Ngoc Phu Tran ◽  
Thang Cong Nguyen ◽  
Duc Hong Vo ◽  
Michael McAleer
Keyword(s):  
At Risk ◽  
Economic Development ◽  
Empirical Evidence ◽  
Empirical Analysis ◽  
Value At Risk ◽  
Oil And Gas ◽  
Market Risk ◽  
Conditional Value At Risk ◽  
Key Industries ◽  
The Empirical Analysis

The purpose of this paper is to evaluate and estimate market risk for the ten major industries in Vietnam. The focus of the empirical analysis is on the energy sector, which has been designated as one of the four key industries, together with services, food, and telecommunications, targeted for economic development by the Vietnam Government through to 2020. The oil and gas industry is a separate energy-related major industry, and it is evaluated separately from energy. The data set is from 2009 to 2017, which is decomposed into two distinct sub-periods after the Global Financial Crisis (GFC), namely the immediate post-GFC (2009–2011) period and the normal (2012–2017) period, in order to identify the behavior of market risk for Vietnam’s major industries. For the stock market in Vietnam, the website used in this paper provided complete and detailed data for each stock, as classified by industry. Two widely used approaches to measure and analyze risk are used in the empirical analysis, namely Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). The empirical findings indicate that Energy and Pharmaceuticals are the least risky industries, whereas oil and gas and securities have the greatest risk. In general, there is strong empirical evidence that the four key industries display relatively low risk. For public policy, the Vietnam Government’s proactive emphasis on the targeted industries, including energy, to achieve sustainable economic growth and national economic development, seems to be working effectively. This paper presents striking empirical evidence that Vietnam’s industries have substantially improved their economic performance over the full sample, moving from relatively higher levels of market risk in the immediate post-GFC period to a lower risk environment in a normal period several years after the end of the calamitous GFC.

scholarly journals Are Value At Risk And Maximum Drawdown Different From Volatility In Stock Market?

2018 ◽  
Vol 34 (2) ◽  
pp. 217-222
Author(s):  
Soo-Hyun Kim
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Cross Sectional ◽  
Additional Risk ◽  
Additional Information ◽  
Maximum Drawdown ◽  
The Empirical Analysis

Measuring risk is the key component in many asset pricing models. Although volatility is the most widely used measure for the risk, Value at Risk (VaR) and Maximum drawdown (MDD) are also considered as alternative risk measure. This article questions whether VaR and MDD contain additional information to volatility in equity market. The empirical analysis is conducted using the stocks listed in Korean stock market. By constructing portfolios in accordance with three risk measures, cross-sectional predictability is tested. The primary findings are as follow; (1) the return patterns are bell shaped in all measures and (2) VaR and MDD do not capture additional risk factors after conditioning volatility.

Value-at-Risk Efficient Portfolio Selection Using Goal Programming

2008 ◽  
Vol 11 (02) ◽  
pp. 187-200 ◽  
Author(s):  
Hsin-Hung Chen
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Goal Programming ◽  
Asset Allocation ◽  
Value At Risk ◽  
Expected Return ◽  
Allocation Model ◽  
Proposed Model ◽  
Portfolio Selection Model ◽  
The Empirical Analysis

The purpose of this study is to apply polynomial goal programming to establish a new portfolio selection model that considers the tradeoffs between expected return and Value-at-Risk (VaR) of portfolios and the flexibility of incorporating investor's preferences. The historical data of 10 international stock markets of Pacific Rim countries were used in the empirical analysis. The results showed that the proposed model demonstrated the ability to resolve the problems of a traditional asset allocation model. The validity and fitness of the proposed model were confirmed.

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Extremwerttheorie und Value-at-Risk

2015 ◽  
Vol 44 (5) ◽  
pp. 259-267
Author(s):  
Frank Schuhmacher ◽  
Benjamin R. Auer
Keyword(s):  
At Risk ◽  
Value At Risk

Value at Risk

Controlling ◽  
2004 ◽  
Vol 16 (7) ◽  
pp. 425-426
Author(s):  
Mischa Seiter ◽  
Sven Eckert
Keyword(s):  
At Risk ◽  
Value At Risk
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