Market Risk Analysis of Energy in Vietnam

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.

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Comparison of market risk models with respect to suggested changes of Basel Accord

Acta Oeconomica ◽

10.1556/aoecon.64.2014.suppl.18 ◽

2014 ◽

Vol 64 (Supplement-2) ◽

pp. 257-274

Author(s):

Eliška Stiborová ◽

Barbora Sznapková ◽

Tomáš Tichý

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Estimation ◽

Market Risk ◽

Capital Requirements ◽

Internal Models ◽

Conditional Value At Risk ◽

Risk Capital ◽

Basel Accord ◽

Capital Charge

The market risk capital charge of financial institutions has been mostly calculated by internal models based on integrated Value at Risk (VaR) approach, since the introduction of the Amendment to Basel Accord in 1996. The internal models should fulfil several quantitative and qualitative criteria. Besides others, it is the so called backtesting procedure, which was one of the main reasons why the alternative approach to market risk estimation — conditional Value at Risk or Expected Shortfall (ES) — were not applicable for the purpose of capital charge calculation. However, it is supposed that this approach will be incorporated into Basel III. In this paper we provide an extensive simulation study using various sets of market data to show potential impact of ES on capital requirements.

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Market-Risk Optimization among the Developed and Emerging Markets with CVaR Measure and Copula Simulation

Risks ◽

10.3390/risks7030078 ◽

2019 ◽

Vol 7 (3) ◽

pp. 78

Author(s):

Trabelsi ◽

Tiwari

Keyword(s):

At Risk ◽

Stock Market ◽

Value At Risk ◽

Risk Measure ◽

Generalized Pareto Distribution ◽

Market Risk ◽

Conditional Value At Risk ◽

Dependence Structure ◽

Stock Market Returns ◽

Pareto Tails

In this paper, the generalized Pareto distribution (GPD) copula approach is utilized to solve the conditional value-at-risk (CVaR) portfolio problem. Particularly, this approach used (i) copula to model the complete linear and non-linear correlation dependence structure, (ii) Pareto tails to capture the estimates of the parametric Pareto lower tail, the non-parametric kernel-smoothed interior and the parametric Pareto upper tail and (iii) Value-at-Risk (VaR) to quantify risk measure. The simulated sample covers the G7, BRICS (association of Brazil, Russia, India, China and South Africa) and 14 popular emerging stock-market returns for the period between 1997 and 2018. Our results suggest that the efficient frontier with the minimizing CVaR measure and simulated copula returns combined outperforms the risk/return of domestic portfolios, such as the US stock market. This result improves international diversification at the global level. We also show that the Gaussian and t-copula simulated returns give very similar but not identical results. Furthermore, the copula simulation provides more accurate market-risk estimates than historical simulation. Finally, the results support the notion that G7 countries can provide an important opportunity for diversification. These results are important to investors and policymakers.

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Pomiar ryzyka rynkowego miarą wartości zagrożonej. Metoda kombinowania prognoz

Kwartalnik Kolegium Ekonomiczno-Społecznego Studia i Prace ◽

10.33119/kkessip.2018.2.9 ◽

2018 ◽

pp. 183-198

Author(s):

Piotr Mazur

Keyword(s):

At Risk ◽

Financial Institutions ◽

Value At Risk ◽

Risk Measure ◽

Market Risk ◽

Risk Measurement ◽

Conditional Value At Risk ◽

Parametric Methods ◽

Combining Forecasts

The article discusses the measurement of market risk by Value at Risk method. Value at Risk measure is an important element of risk measurement mainly for financial institutions but can also be used by other companies. The Value at Risk is presented together with its alternative Conditional Value at Risk. The main methods of VaR estimation were divided into nonparametric, parametric and semi-parametric methods. The next part of the article presents a method of combining forecasts, which can be used in the context of forecasting Value at Risk.

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