Histogram-valued data on value at risk measures: a symbolic approach for risk attribution

Toque, Carole; Terraza, Virginie

doi:10.1080/13504851.2014.920467

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…This study uses the histogram-valued data as a solution for data management, but this approach has a drawback in that we cannot determine which data frequency will lead to the highest hedging effectiveness. In essence and to the best of our understanding, daily closing prices should be used carefully along with data at a wide range of frequencies [30].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

et al. 2021

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Previous studies aimed at determining hedging strategies commonly used daily closing spot and futures prices for the analysis and strategy building. However, the daily closing price might not be the appropriate for price in some or all trading days. This is because the intraday data at various minute intervals, in our view, are likely to better reflect the information about the concrete behavior of the market returns and reactions of the market participants. Therefore, in this study, we propose using high-frequency data along with daily data in an attempt to determine hedging strategies, using five major international currencies against the American dollar. Specifically, in our study we used the 5-min, 30-min, 60-min, and daily closing prices of the USD/CAD (Canadian Dollar), USD/CNY (Chinese Yuan), USD/EUR (Euro), USD/GBP (British Pound), and USD/JPY (Japanese Yen) pairs over the 2018–2019 period. Using data at 5-min, 30-min, and 60-min intervals or high-frequency data, however, means the use of a relatively large number of observations for information extractions in general and econometric model estimations, making data processing and analysis a rather time-consuming and complicated task. To deal with such drawbacks, this study collected the high-frequency data in the form of a histogram and selected the representative daily price, which does not have to be the daily closing value. Then, these histogram-valued data are used for investigating the linear and nonlinear relationships and the volatility of the interested variables by various single- and two-regime bivariate GARCH models. Our results indicate that the Markov Switching Dynamic Copula-Generalized autoregressive conditional heteroskedasticity (GARCH) model performs the best with the lowest BIC and gives the highest overall value of hedging effectiveness (HE) compared with the other models considered in the present endeavor. Consequently, we can conclude that the foreign exchange market for both spot and futures trading has a nonlinear structure. Furthermore, based on the HE results, the best derivatives instrument is CAD using one-day frequency data, while GBP using 30-min frequency data is the best considering the highest hedge ratio. We note that the derivative with the highest hedging effectiveness might not be the one with the highest hedge ratio.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

et al. 2021

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“…CVaR has been used in a wide range of optimization (Alexander et al 2003;Bird et al 2013), risk management, and optimization (Sarykalin et al 2008) problems. Boubaker and Sghaier (2013) studied CVaR in simulations of the dependence structure between financial assets, the adequacy of bank capital (Allen et al 2016), and risk analysis during financial crises (Allen et al 2012;Toque and Terraza 2014).…”

Section: Value-at-risk (Var) and Conditional Value-at-risk (Cvar)mentioning

confidence: 99%

Market Risk Analysis of Energy in Vietnam

Tran

Nguyen

et al. 2019

Risks

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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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Cluster Analysis for Investment Funds Portfolio Optimisation: A Symbolic Data Approach

Terraza

Toque

2021

Financial Risk Management and Modeling

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Histogram-valued data on value at risk measures: a symbolic approach for risk attribution

Cited by 5 publications

References 14 publications

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

Market Risk Analysis of Energy in Vietnam

Cluster Analysis for Investment Funds Portfolio Optimisation: A Symbolic Data Approach

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