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  1. Article ; Online: FX market volatility modelling: Can we use low-frequency data?

    Lyócsa, Štefan / Plíhal, Tomáš / Výrost, Tomáš

    Finance research letters

    2020  Volume 40, Page(s) 101776

    Abstract: High-frequency data tend to be costly, subject to microstructure noise, difficult to manage, and lead to high computational costs. Is it always worth the extra effort? We compare the forecasting accuracy of low- and high-frequency volatility models on ... ...

    Abstract High-frequency data tend to be costly, subject to microstructure noise, difficult to manage, and lead to high computational costs. Is it always worth the extra effort? We compare the forecasting accuracy of low- and high-frequency volatility models on the market of six major foreign exchange market (FX) pairs. Our results indicate that for short-forecast horizons, high-frequency models dominate their low-frequency counterparts, particularly in periods of increased volatility. With an increased forecast horizon, low-frequency volatility models become competitive, suggesting that if high-frequency data are not available, low-frequency data can be used to estimate and predict long-term volatility in FX markets.
    Keywords covid19
    Language English
    Publishing date 2020-09-30
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1544-6131
    ISSN (online) 1544-6131
    DOI 10.1016/j.frl.2020.101776
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Fear of the coronavirus and the stock markets.

    Lyócsa, Štefan / Baumöhl, Eduard / Výrost, Tomáš / Molnár, Peter

    Finance research letters

    2020  Volume 36, Page(s) 101735

    Abstract: Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are ... ...

    Abstract Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are specific to the coronavirus crisis and correspond to phrases related to nonpharmaceutical intervention policies to fight physical contagion. We show that during this period, fear of the coronavirus - manifested as excess search volume - represents a timely and valuable data source for forecasting stock price variation around the world.
    Keywords covid19
    Language English
    Publishing date 2020-08-26
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1544-6131
    ISSN (online) 1544-6131
    DOI 10.1016/j.frl.2020.101735
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds

    Lyócsa, Štefan / Molnár, Peter

    Energy. 2018 July 15, v. 155

    2018  

    Abstract: This paper investigates volatility forecasting for crude oil and natural gas. The main objective of our research is to determine whether the heterogeneous autoregressive (HAR) model of Corsi (2009) can be outperformed by harnessing information from a ... ...

    Abstract This paper investigates volatility forecasting for crude oil and natural gas. The main objective of our research is to determine whether the heterogeneous autoregressive (HAR) model of Corsi (2009) can be outperformed by harnessing information from a related energy commodity. We find that on average, information from related commodity does not improve volatility forecasts, whether we consider a multivariate model, or various univariate models that include this information. However, superior volatility forecasts are produced by combining forecasts from various models. As a result, information from the related commodity can be still useful, because it allows us to construct wider range of possible models, and averaging across various models improves forecasts. Therefore, for somebody interested in precise volatility forecasts of crude oil or natural gas, we recommend to focus on model averaging instead of just including information from related commodity in a single forecast model.
    Keywords energy ; models ; multivariate analysis ; natural gas ; petroleum
    Language English
    Dates of publication 2018-0715
    Size p. 462-473.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 2019804-8
    ISSN 0360-5442 ; 0360-5442
    ISSN (online) 0360-5442
    ISSN 0360-5442
    DOI 10.1016/j.energy.2018.04.194
    Database NAL-Catalogue (AGRICOLA)

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  4. Article: Growth-returns nexus

    Lyócsa, Štefan

    Economic modelling Vol. 42 , p. 343-355

    evidence from three Central and Eastern European countries

    2014  Volume 42, Page(s) 343–355

    Author's details Štefan Lyócsa
    Keywords Real economic activity ; Output growth ; Market volatility ; Emerging markets
    Language English
    Size graph. Darst.
    Publisher Elsevier
    Publishing place Amsterdam [u.a.]
    Document type Article
    ZDB-ID 86824-3
    ISSN 0264-9993
    Database ECONomics Information System

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  5. Article: The effect of non-trading days on volatility forecasts in equity markets

    Lyócsa, Štefan / Molnár, Peter

    Finance research letters Vol. 23 , p. 39-49

    2017  Volume 23, Page(s) 39–49

    Author's details Štefan Lyócsa, Peter Molnár
    Keywords Realized volatility ; Volatility forecasting ; Non-trading days
    Language English
    Publisher Elsevier
    Publishing place Amsterdam [u.a.]
    Document type Article
    ZDB-ID 2181386-3
    ISSN 1544-6123
    Database ECONomics Information System

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  6. Article: Fear of the coronavirus and the stock markets

    Lyócsa, Stefan / Baumöhl, Eduard / Výrost, Tomás / Molnár, Peter

    Financ Res Lett

    Abstract: Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are ... ...

    Abstract Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are specific to the coronavirus crisis and correspond to phrases related to nonpharmaceutical intervention policies to fight physical contagion. We show that during this period, fear of the coronavirus - manifested as excess search volume - represents a timely and valuable data source for forecasting stock price variation around the world.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #733851
    Database COVID19

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    Inter-library loan at ZB MED

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  7. Book ; Online: Social aspirations in European banks

    Lyócsa, Štefan / Baumöhl, Eduard / Výrost, Tomáš

    peer-influenced risk behavior

    2018  

    Abstract: We test a sample of 3,586 banks from 33 European countries to determine whether performances above or below a social aspiration level (median performance of peer banks) influence banks' aggregate risk levels. Our results are consistent with the ... ...

    Author's details Štefan Lyócsa, Tomáš Výrost, Eduard Baumöhl
    Abstract We test a sample of 3,586 banks from 33 European countries to determine whether performances above or below a social aspiration level (median performance of peer banks) influence banks' aggregate risk levels. Our results are consistent with the behavioral theory of the firm and prospect theory in that we find that bank performance below a bank's social aspiration level is followed by increased aggregate risk, i.e., risk-taking behavior in the subsequent year. Although under-performing banks tend to be risk-takers, large banks and banks with high aggregate risk levels tend to limit the increase in their aggregate risk levels.
    Keywords social aspiration ; European banks ; performance ; risk behavior ; prospect theory
    Language English
    Size 1 Online-Ressource (circa 10 Seiten), Illustrationen
    Document type Book ; Online
    Database ECONomics Information System

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  8. Article: Stock market oscillations during the corona crash: The role of fear and uncertainty

    Lyocsa, Stefan / Molnar, Peter

    Finance Research Letters

    Abstract: Stock market returns are difficult to predict, but crisis periods tend to be an exception to this rule We document that, during the event period from November 2019 to May 2020 with the S&P 500 market index, the corona crash was not an exception We use a ... ...

    Abstract Stock market returns are difficult to predict, but crisis periods tend to be an exception to this rule We document that, during the event period from November 2019 to May 2020 with the S&P 500 market index, the corona crash was not an exception We use a nonlinear autoregressive model, where the autoregressive coefficient is governed by i) abnormal Google searches related to COVID-19 and ii) realized volatility We find that the autoregressive coefficient was negative over the whole event period, but as market uncertainty and attention to virus increased, the magnitude of the autoregressive coefficient increased as well
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #816475
    Database COVID19

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    Inter-library loan at ZB MED

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  9. Book ; Article ; Online: Fear of the coronavirus and the stock markets

    Lyócsa, Štefan / Baumöhl, Eduard / Výrost, Tomáš / Molnár, Peter

    2020  

    Abstract: Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines, which have resulted in extremely high stock market uncertainty, measured as price variation. In this paper, we show that during such ... ...

    Abstract Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines, which have resulted in extremely high stock market uncertainty, measured as price variation. In this paper, we show that during such periods, Google Trends data represent a timely and valuable data source for forecasting price variation. Fear of the coronavirus, as measured by Google searches is predictive of future stock market uncertainty for stock markets around the world. Google searches were also strongly correlated with the evolution of physical contagion (the number of new cases), and with implemented nonpharmaceutical interventions. The effect of pandemic-related policies on investors' attention and fear is thus very well captured by Google Trends data.
    Keywords ddc:330 ; G01 ; G15 ; Coronavirus ; Stock market ; Uncertainty ; Panic ; Google Trends ; covid19
    Language English
    Publisher Kiel, Hamburg: ZBW – Leibniz Information Centre for Economics
    Publishing country de
    Document type Book ; Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  10. Article: Risk-return convergence in CEE stock markets

    Lyócsa, Štefan / Baumöhl, Eduard

    Finance a úvěr Vol. 64, No. 5 , p. 352-373

    structural breaks and market volatility

    2014  Volume 64, Issue 5, Page(s) 352–373

    Author's details Štefan Lyócsa; Eduard Baumöhl
    Keywords risk-return characteristics ; convergence ; stock market integration ; market volatility
    Language English
    Size graph. Darst.
    Publisher Economia
    Publishing place Praha
    Document type Article
    ZDB-ID 860318-2 ; 2463945-X
    ISSN 0015-1920
    ISSN 0015-1920
    Database ECONomics Information System

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