Realized volatility models and alternative Value-at-Risk prediction strategies

Louzis, Dimitrios P.; Xanthopoulos-Sisinis, Spyros; Refenes, Apostolos P.

doi:10.1016/j.econmod.2014.03.025

Cited by 56 publications

(25 citation statements)

References 62 publications

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“…Şener et al (2012) tested and ranked twelve different popular VaR models on the equity indexes of four European emerging markets, and found that asymmetric methods, such as CAViaR Asymmetric, generate the best performing VaR estimates. Similar findings were presented by Louzis et al (2014). Their results provide evidence in favor of the Asymmetric HAR (Heterogeneous Autoregressive) realized volatility model combined with the EVT.…”

Section: Literature Reviewsupporting

confidence: 77%

Quantifying extreme market risk in the selected Western Balkan countries

Radivojević¹,

Ćurčić²,

Marčetić³

2018

Industrija

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Section: Literature Reviewsupporting

confidence: 77%

Quantifying extreme market risk in the selected Western Balkan countries

Radivojević¹,

Ćurčić²,

Marčetić³

2018

Industrija

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“…Although there is an abundance of research papers that examine performances of the various HS model, a small number of these papers deal with the FHS model, especially in the emerging markets and frontier markets. The most important studies are conducted by Rossignolo et al (2012Rossignolo et al ( , 2013, Louzis et al (2014), Zikovic (2010) and Radivojevic et al (2016b).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The common feature of these models is that they do not behave as it would be expected based on the procedures behind the models (see Stancic et al 2013); they are computationally very intensive because they require a relatively large number of parameters that cannot be solved in a closed, analytical form and can result in negative values, where both problems have a negative influence on the maximum estimated likelihood. On the other hand, the numerous empirical researches (Zikovic 2007;Diamandis et al 2011;Şener et al 2012;Rossignolo et al 2012Rossignolo et al , 2013Cui et al 2013;Louzis et al 2014;Del Brio et al 2014) show that filtered historical simulation which was proposed by Hull and White (further marked as FHS) performs better than the commonly used and the most popular VaR models at both markets (developed and emerging) in the context of meeting the backtesting rules of the Basel Committee. Theoretically, the FHS model represents improvement of the HS model, concerning the ability to capture time-varying volatility (conditional heteroscedasticity), without a significant increase in computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Hull-White’s Value at Risk Model: Case Study of Baltic Equities Market

Radivojević

Ćurčić

Vukajlović

2017

Journal of Business Economics and Management

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Analysis of the applicability of the Hull and White (FHS) model on the Baltic equities market has not been the subject of significant research, especially not in the context of meeting the Basel Committee backtesting rules. The paper discusses the applicability of different variants of this model, in order to answer the question whether any variants (and which of them) of the model can be used in these markets in the context of the Basel II and III standards. The survey results show that 1) there isn’t an optimal variant of this model, but that risk managers have to keep in mind stylized facts of financial returns when they specify the FHS model; 2) according to different criteria of the validity of the model (Basel II and III standards) different variants of models are differently ranked, which suggests that selection of a suitable model implies the use of a large number of different criteria, the model validity and loss function, especially those who take care of the size of tail loss and ES.

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“…In addition, many researchers have employed high frequency data to analyze the value at risk (Intraday VaR) such as Dionne, Shao in 2009 and Louzis in 2014 (Dionne et al, 2009, Shao et al, 2009and Louzis et al, 2014, Aloui et al in 2010 on analysis of energy value in energy products (Aloui et al, 2010), and Tian et al in 2017 that dealt with value at risk in agricultural commodities futures using high frequency data and HAR models (Tian et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Realized Volatility in Agricultural Commodities Futures

Rattanasamakarn¹,

Tansuchat²

2018

Proceedings of the 9th Economics &Amp; Finance Conference, London

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The objectives of this paper are to construct the efficient frontier and optimum portfolio of agricultural commodity futures, and to evaluate financial risk by Value at Risk. We evaluated alternative volatility forecasting and computed daily Value at Risk (VaR) based on Realized Volatility approach and ARFIMA-FIGARCH model. The intraday trade data of three agricultural commodity futures prices, namely corn, wheat and soybean traded in the Chicago Board of Trade (CBOT) with three different frequencies namely 1 minute, 5 minutes and 15 minutes, were collected from Bloomberg database. The complete data set covered the period from November 2015 to December 2016. The empirical results showed that the calculated realized volatility from Realized Covariance Measure (Andersen et al. 2003) of corn, wheat and soybean futures returns have the long memory feature for every frequency based on R/S test and GPH test. The simulated returns from ARFIMA-FIGARCH are applied to construct the efficient frontier and optimum portfolio. The optimum portfolio suggested investing more than half in corn followed by soybean and wheat, respectively.

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Realized volatility models and alternative Value-at-Risk prediction strategies

Cited by 56 publications

References 62 publications

Quantifying extreme market risk in the selected Western Balkan countries

Quantifying extreme market risk in the selected Western Balkan countries

Hull-White’s Value at Risk Model: Case Study of Baltic Equities Market

Realized Volatility in Agricultural Commodities Futures

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