FX market volatility modelling: Can we use low-frequency data?

Lyócsa, Štefan; Plíhal, Tomáš; Výrost, Tomáš

doi:10.1016/j.frl.2020.101776

Cited by 11 publications

(8 citation statements)

References 59 publications

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“… Huang and Zheng (2020) examine the relationship between investor sentiment and crude oil futures price and find evidence of a cointegrating relationship between them. Another strand of literature focuses on the impact of the pandemic on financial and foreign exchange markets ( Bouri et al, 2021 , Corbet et al, 2020 , Garg and Prabheesh, 2021 , Lyócsa et al, 2020 , Mishra et al, 2020 , Narayan, 2020b , Narayan et al, 2020 , Prabheesh, 2020 , Rai and Garg, 2022 , Wei et al, 2020 , Zaremba et al, 2020 )…”

Section: Review Of Literaturementioning

confidence: 99%

Has COVID-19 intensified the oil price–exchange rate nexus?

Chowdhury

Garg

2022

Economic Analysis and Policy

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Section: Review Of Literaturementioning

confidence: 99%

Has COVID-19 intensified the oil price–exchange rate nexus?

Chowdhury

Garg

2022

Economic Analysis and Policy

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“…Chou, 2005;Brandt & Jones, 2006;Li & Hong, 2011;Fiszeder & Perczak, 2016;Molnár, 2016;. The application of low and high prices can be beneficial even compared to the use of high frequency data (see Degiannakis & Livada, 2013;Lyócsa et al, 2021a;2021b). According to our knowledge, the range-based volatility models have not been applied to analyze crises periods related to wars so far.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies

Fiszeder

Małecka

2022

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Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had the adverse effect on the accuracy of volatility forecasts. The scale of negative effects of war was, however, market-specific and some markets exhibited a strong tendency to return to usual levels in a short time. Purpose of the article: We study the volatility shocks caused by the war. Our focus is on the markets highly exposed to the effects of this conflict: the stock, currency, cryptocurrency, gold, wheat and crude oil markets. We evaluate the forecasting accuracy of volatility models during the first stage of the war and compare the scale of forecast deterioration among the examined markets. Our long-term purpose is to analyze the methods that have the potential to mitigate the effect of forecast deterioration under such circumstances. We concentrate on the methods designed to deal with outliers and periods of extreme volatility, but, so far, have not been investigated empirically under the conditions of war. Methods: We use the robust methods of estimation and a modified Range-GARCH model which is based on opening, low, high and closing prices. We compare them with the standard maximum likelihood method of the classic GARCH model. Moreover, we employ the MCS (Model Confidence Set) procedure to create the set of superior models. Findings & value added: Analyzing the market specificity, we identify both some common patterns and substantial differences among the markets, which is the first comparison of this type relating to the ongoing conflict. In particular, we discover the individual nature of the cryptocurrency markets, where the reaction to the outbreak of the war was very limited and the accuracy of forecasts remained at the similar level before and after the beginning of the war. Our long-term contribution are the findings about suitability of methods that have the potential to handle the extreme volatility but have not been examined empirically under the conditions of war. We reveal that the Range-GARCH model compares favorably with the standard volatility models, even when the latter are evaluated in a robust way. It gives valuable implication for the future research connected with military conflicts, showing that in such period gains from using more market information outweigh the benefits of using robust estimators.

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“…Low-frequency volatility models are competitive. Hence, low-frequency data can be utilized to estimate and predict volatility in stock markets (Lyócsa et al 2021). The pandemic sample starts in January 2020 and ends in January 2022.…”

Section: Datamentioning

confidence: 99%

Stock Market Volatility in Zimbabwe Stock Exchange During Pandemic Period

Bonga¹

2022

EJEF

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Volatility is essential to consider uncertainty surrounding investments in financial assets. For this reason, financial industry regulators, mutual fund managers, individual and institutional investors, and policymakers are concerned about volatility. Against this background, this paper investigates the volatility of returns on the Zimbabwean stock market between January 2020 and January 2022. We use the All Shares Index for Zimbabwe Stock Exchange (ZSE) for volatility analysis and perform the quantitative investigation using GARCH family models. According to the AIC and SIC criterion, we use the GARCH (1,1) model and perform a complete analysis considering the results obtained from EGARCH (1,1) and IGARCH (1,1) regressions. Results report persistence in volatility, showing that it takes time for the market to digest information into the prices fully, and the shocks to conditional variance take longer to die out. Also, an asymmetry exists, implying that bad news and good news impact differently on the stock market, and the magnitude of volatility due to the good news is higher than bad news. Therefore, we conclude that positive news of the same magnitude impacts more than bad news. Investors rely more on the good news for effective decisions during the pandemic to earn more. Considering the results, any policy aimed at reducing the impact of the pandemic is favorable for investment.

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FX market volatility modelling: Can we use low-frequency data?

Cited by 11 publications

References 59 publications

Has COVID-19 intensified the oil price–exchange rate nexus?

Has COVID-19 intensified the oil price–exchange rate nexus?

Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies

Stock Market Volatility in Zimbabwe Stock Exchange During Pandemic Period

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