Volatility Forecasting in Futures Markets: A Statistical and Value-at-Risk Evaluation

2015 ◽  
Author(s):  
Theo Athanasiadis
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Evaluation ◽  
Futures Markets ◽  
Volatility Forecasting

Return-Risk Evaluation of Investments

2020 ◽  
pp. 161-177
Author(s):  
Paul Weirich
Keyword(s):  
At Risk ◽  
Probability Distribution ◽  
Value At Risk ◽  
Risk Evaluation ◽  
Expected Return ◽  
Factors Affecting ◽  
Special Cases ◽  
Mean Risk

In finance, a common way of evaluating an investment uses the investment’s expected return and the investment’s risk, in the sense of the investment’s volatility, or exposure to chance. A version of this method derives from a general mean-risk evaluation of acts, under the assumption that only money, risk, and their sources matter. Although the method does not require a measure of risk, finance investigates measures of risks to assist evaluations of risks. An investment creates possible returns, and the variance of the probability distribution of their utilities is a measure of the investment’s risk. This measure neglects some factors affecting an investment’s risk, and so is satisfactory only in special cases. Another measure of risk is known as value-at-risk, or VAR. It also neglects some factors affecting an investment’s risk, and so should be restricted to special cases.

scholarly journals An analytical method of estimating Value-at-Risk on the Belgrade Stock Exchange

2009 ◽  
Vol 54 (183) ◽  
pp. 119-138 ◽  
Author(s):  
Milica Obadovic ◽  
Mirjana Obadovic
Keyword(s):  
At Risk ◽  
Failure Rate ◽  
Confidence Level ◽  
Analytical Method ◽  
Value At Risk ◽  
Risk Evaluation ◽  
Risk Model ◽  
Stock Exchange ◽  
Confidence Levels ◽  
The Given

This paper presents market risk evaluation for a portfolio consisting of shares that are continuously traded on the Belgrade Stock Exchange, by applying the Value-at-Risk model - the analytical method. It describes the manner of analytical method application and compares the results obtained by implementing this method at different confidence levels. Method verification was carried out on the basis of the failure rate that demonstrated the confidence level for which this method was acceptable in view of the given conditions.

scholarly journals Volatility forecasting and value-at-risk estimation in emerging markets: the case of the stock market index portfolio in South Africa

2011 ◽  
Vol 12 (4) ◽  
pp. 401-411 ◽  
Author(s):  
Lumengo Bonga-Bonga ◽  
George Mutema
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Emerging Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Volatility Forecasting ◽  
Stock Market Index ◽  
Market Index ◽  
Auto Regressive ◽  
Asymmetric Behaviour

Accurate modelling of volatility is important as it relates to the forecasting of Value-at-Risk (VaR). The RiskMetrics model to forecast volatility is the benchmark in the financial sector. In an important regulatory innovation, the Basel Committee has proposed the use of an internal method for modelling VaR instead of the strict use of the benchmark model. The aim of this paper is to evaluate the performance of RiskMetrics in comparison to other models of volatility forecasting, such as some family classes of the Generalised Auto Regressive Conditional Heteroscedasticity models, in forecasting the VaR in emerging markets. This paper makes use of the stock market index portfolio, the All-Share Index, as a case study to evaluate the market risk in emerging markets. The paper underlines the importance of asymmetric behaviour for VaR forecasting in emerging markets’ economies.

scholarly journals Risk evaluation on leading companies in property and real estate subsector at IDX: A Value-at-Risk with ARMAX-GARCHX approach and duration test

2018 ◽  
Vol 979 ◽  
pp. 012094 ◽  
Author(s):  
Dedy Dwi Prastyo ◽  
Dwi Handayani ◽  
Soo-Fen Fam ◽  
Santi Puteri Rahayu ◽  
Suhartono ◽  
...  
Keyword(s):  
At Risk ◽  
Real Estate ◽  
Value At Risk ◽  
Risk Evaluation ◽  
A Value

Clearing margin system in the futures markets—Applying the value-at-risk model to Taiwanese data

2006 ◽  
Vol 367 ◽  
pp. 353-374 ◽  
Author(s):  
Chien-Liang Chiu ◽  
Shu-Mei Chiang ◽  
Jui-Cheng Hung ◽  
Yu-Lung Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Futures Markets ◽  
Risk Model

Examining the evidence of risk spillovers between Shanghai and London non-ferrous futures markets: a dynamic Copula-CoVaR approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hong Shen ◽  
Yue Tang ◽  
Ying Xing ◽  
Pin Ng
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Global Economy ◽  
Futures Markets ◽  
Ferrous Metal ◽  
Conditional Value At Risk ◽  
Metal Exchange ◽  
London Metal Exchange ◽  
Content Type ◽  
Dynamic Copula

PurposeThis paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.Design/methodology/approachWith daily data, the marginal distributions and optimal Copula functions are determined using the kernel estimation method and squared Euclidean distance test. The conditional value-at-risk and the conditional value-at-risk spillover rate are computed from the Copula estimated parameters based on the Copula-CoVaR model. Also, the dynamic correlation coefficient between the two futures markets is investigated.FindingsThe empirical results are as follows: overall, the risk spillover effect exerted by the London Metal Exchange on the Shanghai Futures Exchange is more significant than vice versa. Moreover, the degree of risk spillovers exerted by the London Metal Exchange on the Shanghai Futures Exchange for zinc and copper are more significant when they are depressed in the London Metal Exchange. Moreover, the dynamic of the correlation between the Shanghai and London futures markets is attributed to be largely due to changes in the global economy.Research limitations/implicationsThe Copula-CoVaR model used in this paper is suitable for measuring the risk spillovers between two different markets, while the risk spillovers across multiple markets or the consideration of multiple risk factors cannot be accurately captured using this framework. Multiple state variables to capture time variation in the conditional moments of return series will be a topic in future research.Practical implicationsThe results provide theoretical support for risk management and monitoring of the non-ferrous futures markets.Originality/valueThe ability of the Copula function to accurately describe a nonlinear relationship and tail correlation is harnessed to measure the risk spillovers, explore the degree and direction of risk spillovers and identify the source of risk spillovers. The global economy is incorporated as a macro factor to explore its inner connection with the dynamic of risk spillovers in the non-ferrous metal futures market.

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