Value at Risk of the main stock market indexes in the European Union (2000–2012)

Iglesias, Emma M.

doi:10.1016/j.jpolmod.2015.01.006

Cited by 8 publications

(8 citation statements)

References 31 publications

(39 reference statements)

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“…In order to check the robustness of the results, following Iglesias (2015) , we estimated a Vector Autoregressive Regression (VAR) model with heteroscedasticity-robust standard errors where all the returns of the different countries were the endogenous variables with also 1 lag (many of the 1-lagged parameter estimates were statistically significant), and as exogenous variables we considered the returns of the MSCI and the six dummy variables Dj that are equal to 1 on j = 24, 27, 28 of June 2016 and j = 12, 13, 16 of March 2020. We excluded from this VAR analysis Chile, Sweden and Finland because they did not have some of the returns available during the considered six dummy variables.…”

Section: Models and Empirical Resultsmentioning

confidence: 99%

“…In relation to the selected countries, we use daily data 2 of returns of a total of 24 countries that include the same eleven European countries included in Iglesias (2015) (we use the value at the moment of the closing of each daily session). Following Burdekin et al (2018) , we also add stock market indexes for the necessary countries to study the PIIGS, BRICS (Brazil, Russia, India, China, and South Africa) and some other parts of America (Brazil, Canada, Chile, United States), Asia/Oceania (Australia, India, China and Japan) and Africa (South Africa).…”

Section: Datamentioning

confidence: 99%

“…Following Iglesias (2015) , if we denote p t the stock market index of country p at period t , then we construct the return of country p at period t (denoted R p t ) as Rp t = ln(p t /p t-1 ) where t = 1,…,T and T is all the available sample size. We also denote RMSCI t = ln(MSCI t /MSCI t-1 ).…”

Section: Datamentioning

confidence: 99%

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The influence of extreme events such as Brexit and Covid-19 on equity markets

Iglesias

2022

Journal of Policy Modeling

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Section: Models and Empirical Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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The influence of extreme events such as Brexit and Covid-19 on equity markets

Iglesias

2022

Journal of Policy Modeling

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“…Multiple research has been undertaken on VaR in emerging Southeast Asian countries (Cheong et al, 2011), the European Union nations (Iglesias, 2015), the Latin American countries (Ozun & Cifter, 2007), Nordic markets (Jobayed, 2017), the South African market (Mabitsela et al, 2015;Naradh et al, 2021), and others. For the period 2000-2012, Iglesias (2015) researched VaR based on major equities asset market indices of 11 EU nations. Glosten et al (1993) used a GJR-GARCH model and estimated the returns of those 11 market indices.…”

Section: Literature Reviewmentioning

confidence: 99%

Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets

Shaik

Padmakumari

2022

Investment Management and Financial Innovations

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Value-at-risk (VaR) is the most common and widely used risk measure that enterprises, particularly major banking corporations and investment bank firms employ in their risk mitigation processes. The purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on BRICS (Brazil, Russia, India, China, South Africa) and US stock market indices. The study employs three different VaR estimation models, namely normal (N), historical (HS), exponential weighted moving average (EMWA) procedures, and eight backtesting models. The empirical analysis is conducted during three different periods: overall period (2006–2021), global financial crisis (GFC) period (2008–2009), and COVID-19 period (2020–2021). The results show that the EMWA model performs better compared to N and HS estimation models for all the six stock market indices during overall and crisis sample periods. The results found that VaR models perform poorly during crisis periods like GFC and COVID-19 compared to the overall sample period. Furthermore, the study result shows that the predictive accuracy of VaR methods is weak during the COVID-19 era when compared to the GFC period.

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“…VaR constitutes the central risk measure method for financial markets (Iglesias, 2015;Karmakar & Paul, 2015;Youssef, Belkacem, & Mokni, 2015) and has been proved to be an effective way to measure carbon market risks (Feng et al, 2012;Jiang et al, 2015). VaR represents the maximum expected loss of a single asset or a portfolio over a given holding period at a specified confidence level.…”

Section: Value-at-risk (Var)mentioning

confidence: 99%

Enriching the VaR framework to EEMD with an application to the European carbon market

et al. 2018

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Unlike common financial markets, the European carbon market is a typically heterogeneous market, characterized by multiple timescales, and affected by extreme events. The traditional value‐at‐risk (VaR) with single‐timescale fails to deal with the multi‐timescale characteristics and the effects of extreme events, which can result in the VaR overestimation for carbon market risk. To measure accurately the risk on the European carbon market, we propose an ensemble empirical mode decomposition (EEMD)‐based multiscale VaR approach. First, the EEMD algorithm is utilized to decompose the carbon price return into several intrinsic mode functions (IMFs) with different timescales and a residue, which are modelled, respectively, using the ARMA‐Generalized Autoregressive Conditional Heteroscedasticity model to obtain their conditional variances at different timescales. Furthermore, the Iterated Cumulative Sums of Squares algorithm is employed to determine the windows of an extreme event, so as to identify the IMFs influenced by an extreme event and conduct an exponentially weighted moving average on their conditional variations. Finally, the VaRs of various IMFs and the residue are estimated to reconstruct the overall VaR, the validity of which is verified later. Then, we illustrate the results by considering several European carbon futures contracts. Compared with the traditional VaR framework with single timescale, the proposed multiscale VaR‐EEMD model can effectively reduce the influences of the heterogeneous environments (such as the influences of extreme events) and obtain a more accurate overall risk measure on the European carbon market. By acquiring the distributions of carbon market risks at different timescales, the proposed multiscale VaR‐EEMD estimation is capable of understanding the fluctuation characteristics more comprehensively, which can provide new perspectives for exploring the evolution law of the risks on the European carbon market.

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Value at Risk of the main stock market indexes in the European Union (2000–2012)

Cited by 8 publications

References 31 publications

The influence of extreme events such as Brexit and Covid-19 on equity markets

The influence of extreme events such as Brexit and Covid-19 on equity markets

Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets

Enriching the VaR framework to EEMD with an application to the European carbon market

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