Hunting the quicksilver: Using textual news and causality analysis to predict market volatility

Banerjee, Ameet Kumar; Dionísio, Andreia; Pradhan, H. K.; Mahapatra, B. S.

doi:10.1016/j.irfa.2021.101848

Cited by 21 publications

(7 citation statements)

References 41 publications

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“…Extant literature has investigated the impact of different news signals (Nowak et al ., 2011; Banerjee et al ., 2021; Banerjee and Pradhan, 2022) or the effects of the financial crisis on asset pricing (Wahidin et al ., 2021; Hasan et al ., 2022). Research interest has been directed towards areas dealing with conflicts; given the growing incidence of war scenarios, studies have precisely looked into the conflict's impact on the stock markets (Boubaker et al ., 2022; Boungou and Yatié, 2022).…”

Section: Introductionmentioning

confidence: 99%

Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR

Banerjee

2023

JRF

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PurposeThis paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.Design/methodology/approachThe author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.FindingsThe study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.Practical implicationsThe paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.Originality/valueTo the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.

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Section: Introductionmentioning

confidence: 99%

Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR

Banerjee

2023

JRF

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“…The more the media spar and penned about COVID-19, the more the markets overreacted, and past literature has established numerous such evidence between overreaction to news and financial markets (Banerjee and Padhan, 2017; Banerjee et al. , 2020; Banerjee and Pradhan, 2020; Banerjee et al. , 2021; Banerjee and Pradhan, 2022; Barberis et al.…”

Section: Introductionmentioning

confidence: 99%

“…, 2020). Banerjee et al. (2021) and Banerjee and Pradhan (2022), using both quantitative and qualitative news, have documented the bond market reaction to news signals whereas Nozawa and Qiu (2021) looked into the behaviour of corporate bonds during the pandemic period.…”

Section: Introductionmentioning

confidence: 99%

“…Every day, the media spurts a new exciting development about COVID-19, markets immediately unveil cold, cough and breathlessness signals. The more the media spar and penned about COVID-19, the more the markets overreacted, and past literature has established numerous such evidence between overreaction to news and financial markets (Banerjee and Padhan, 2017;Banerjee et al, 2021;Banerjee and Pradhan, 2022;Barberis et al, 1998;Elder et al, 2012). Amid this chaos, the atypical manifestation of financial markets occurred, with leading market indicators showing uncharacteristic behaviour.…”

Section: Introductionmentioning

confidence: 99%

“…The asset pricing theory has shed light on such behavioural drivers to explain market behaviour (Tetlock, 2007;Kaplanski and Levy, 2010;Garcia, 2013;Rognone et al, 2020). Banerjee et al (2021) and Banerjee and Pradhan (2022), using both quantitative and qualitative news, have documented the bond market reaction to news signals whereas Nozawa and Qiu (2021) looked into the behaviour of corporate bonds during the pandemic period.…”

Section: Introductionmentioning

confidence: 99%

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You sneeze, and the markets are paranoid: the fear, uncertainty and distress sentiments impact of the COVID-19 pandemic on the stock–bond correlation

Banerjee

2022

JRF

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PurposeThis paper investigates the influence of three different sentiment indicators on the time-varying stock–bond correlation of 15 countries during the global crisis period of the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approachThe author uses the time-varying correlation estimated using the autoregressive moving average -dynamic conditional correlation - generalised autoregressive conditional heteroskedasticity (ARMA-DCC-GARCH) model to achieve this aim. The impact of investor sentiment on the stock–bond correlation was analysed using the Markov regime-switching regression.FindingsThe study results show that the sentiment indicators of fear, uncertainty and distress have a pronounced negative impact on the stock–bond correlation. They further provide evidence of a strong regime effect on the stock–bond correlation with sentiment indicators.Practical implicationsThe paper has a relevant impact on policymakers and fund managers. First, the policymakers now have more insightful evidence of how the stock and bond markets react during crises. Second, the fund managers need to focus on behavioural variables as they may be driving factors in crisis periods that may impair portfolio management.Originality/valueTo the best of my knowledge, the paper is the first to throw light on the behaviour of the stock–bond correlation for 15 countries during the COVID-19 period.

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Disentangling the Nonlinearity Effect in Cryptocurrency Markets During the Covid-19 Pandemic: Evidence from a Regime-Switching Approach

Mgadmi¹,

Béjaoui

Moussa³

2022

Asia-Pac Financ Markets

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In this paper, we attempt to understand and identify the cyclical fluctuations in cryptocurrency markets. To this end, we apply the Markov-Switching approach on daily prices of 17 selected digital currencies. This model allows us to capture the nonlinear structure in cryptocurrencies’ prices. The empirical results clearly show potential difference(s) among digital currencies when they react to the varying levels of the pandemic's severity. The existence of two distinguishable states and each state seems to be characterized by different features of market cycle’s phase for each cryptocurrency. So, the Covid19 pandemic affects asymmetrically the different market phases of digital currencies. Such findings can have insightful portfolios implications.

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Hunting the quicksilver: Using textual news and causality analysis to predict market volatility

Cited by 21 publications

References 41 publications

Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR

Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR

You sneeze, and the markets are paranoid: the fear, uncertainty and distress sentiments impact of the COVID-19 pandemic on the stock–bond correlation

Disentangling the Nonlinearity Effect in Cryptocurrency Markets During the Covid-19 Pandemic: Evidence from a Regime-Switching Approach

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