What Drives Bitcoin’s Price Crash Risk?

Kalyvas, Antonios Nikolaos; Papakyriakou, Panayiotis; Σάκκας, Αθανάσιος; Urquhart, Andrew

doi:10.2139/ssrn.3474550

Cited by 17 publications

(27 citation statements)

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“…Recent studies differ in the econometric methodologies proposed to study the dynamics of the cryptocurrencies and also on the set of factors used to rationalise its behaviour. We highlight important contributions by Ciaian et al (2015), Hayes (2017), Kristoufek (2015), Polasik, Piotrowska, Wisniewski, Kotkowski, and Lightfoot (2015), Zhu, Dickinson, and Li (2017), Blau (2017), Zhang, Wang, Li, and Shen (2018), Bouri, Gupta, Lahiani, and Shahbaz (2018), Jareno, Gonzales, Tolentino, and Sierra (2020) and Kalyvas, Papakyriakou, Sakkas, and Urquhart (2020). These authors use a variety of econometric models such as OLS, VECM, Wavelet Analysis, IV Estimation, GARCH and MF-DCCA models and consider market and technical variables to explain the dynamics of cryptocurrencies.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Bitcoin prices using market and sentiment variables

Kapar

Olmo

2020

World Economy

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There is an emerging empirical stream of literature that has attempted to explain the mechanisms behind cryptocurrency pricing and, in particular, Bitcoin. This paper belongs to this recent tradition; however, in contrast to most empirical studies on the dynamics of the cryptocurrency, we focus on uncovering its efficient price. Informally, the efficient price can be interpreted as an average price that reflects the long-run dynamics of the price after removing short-term fluctuations due to market noise, outliers or the occurrence of other transitory disruptions in the market. The contribution of this paper is twofold. First, we identify the long-run relationship between Bitcoin and a set of variables with power to explain the dynamics of the cryptocurrency. This is done applying cointegration methods. If Bitcoin is cointegrated with any of the other variables considered, it implies that there is a long-run relationship between them. This can be used by investors to make strategic trading decisions. When there is a deviation from the long-run equilibrium, investors can act on that in the belief that it will return to the long-run equilibrium. The second contribution is to explore the factors with ability to explain the efficient Bitcoin price. More formally, we define the efficient Bitcoin price as the permanent component of the cryptocurrency log-price obtained from the permanent-transitory (P-T) decomposition proposed in Gonzalo and Granger (1995). To the best of our knowledge, this is the first paper that identifies the efficient price of Bitcoin by applying the permanent-transitory (P-T) decomposition proposed by these authors. Our empirical strategy is based on estimating a vector error correction model (VECM), see Johansen (1988, 1991), to assess the long-run relationship between the Bitcoin spot price and a set of financial, economic and sentiment variables. Motivated by the empirical literature exploring the determinants of Bitcoin and also by the properties of cryptocurrencies, we entertain the following 46 | KAPAR And OLMO variables: (a) the S&P 500 index capturing the performance of financial markets, (b) the price of gold, a commodity with investment properties similar to those characterising Bitcoin, (c) Google Search as a variable that quantifies the entries in the Google engine associated to Bitcoin, in particular, and cryptocurrencies, in general, and (d) a fear index proxied by the FED Financial Stress Index that captures market sentiment. The VECM specification is applied to two overlapping periods characterised by very different dynamics. A first period considering data up to January 2018, and a second period that considers data up to May 2019 characterised by a steep decline in the Bitcoin price and a slow recovery. The findings of our empirical study are in stark contrast across periods. For the first period, we find strong positive comovements between the dynamics of Bitcoin price and online interest in the cryptocurrency measured by Google searches. The other factor with a positive relatio...

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Bitcoin prices using market and sentiment variables

Kapar

Olmo

2020

World Economy

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“…In this line, Onan et al. (2014) find that good and bad announcements asymmetrically impact the financial volatility, whereas most of recent studies focus on the role of Economic Policy Uncertainty (EPU) in influencing the financial volatility ( Antonakakis et al., 2013 ; Chen and Chiang, 2020 ; Kalyvas et al., 2019 ; Li et al., 2020 ; Mei et al., 2018 ; Su et al., 2019 ; Tiwari et al., 2019 ; Yen and Cheng, 2020 ; Zhenghui and Junhao, 2019 ). For example, Karnizova and Li (2014) predict the US recession using the interaction between EPU and stock market volatility, whereas Zhu et al.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and the United States financial markets’ volatility

Albulescu

2021

Finance Research Letters

429

265

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We empirically investigate the effect of the official announcements regarding the COVID-19 new cases of infection and fatality ratio, on the financial markets volatility in the United States (US). We consider both COVID-19 global and US figures and show that the sanitary crisis enhances the S&P 500 realized volatility. Our findings are robust to different model specifications and suggest that the prolongation of the coronavirus pandemic is an important source of financial volatility, challenging the risk management activity.

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“…Gronwald (2019) shows that Bitcoin price dynamics is particularly influenced by extreme price movements, more so than in the markets for crude oil or gold, which is possibly a result of the immaturity of the market. Kalyas, Papakyriakou, Sakkas, and Urquhart (2019) show that the Bitcoin crash risk is lower when economic policy uncertainty is high, indicating the hedging ability of Bitcoin against economic policy uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks

Shen

Urquhart

Wang

2020

Euro Fin Management

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This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. We show the importance of the decomposition of realized variance in the in‐sample regressions using 18 competing heterogeneous autoregressive (HAR) models. In the out‐of‐sample setting, we find that the HARQ‐F‐J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method.

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What Drives Bitcoin’s Price Crash Risk?

Cited by 17 publications

References 0 publications

Analysis of Bitcoin prices using market and sentiment variables

Analysis of Bitcoin prices using market and sentiment variables

COVID-19 and the United States financial markets’ volatility

Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks

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