Do FOMC and macroeconomic announcements affect Bitcoin prices?

Pyo, Sujin; Lee, Jae‐Wook

doi:10.1016/j.frl.2019.101386

Cited by 40 publications

(29 citation statements)

References 23 publications

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“…Bouri et al ( 2017 ) also document a weak connection between bitcoin and other fundamental financial variables, such as major world stock indices, bonds, oil, gold, the general commodity index, and the U.S. dollar index. Pyo and Lee ( 2019 ) find no relationship between bitcoin prices and announcements on employment rate, Producer Price Index, and CPI in the United States; however, their results suggest that bitcoin reacts to announcements of the Federal Open Market Committee on U.S. monetary policy.…”

Section: Literature Reviewmentioning

confidence: 92%

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

2021

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This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

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Section: Literature Reviewmentioning

confidence: 92%

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

2021

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“…Bitcoin and cryptocurrencies have been designed to be detached from any conventional monetary systems; thus, we present the first attempt to explore whether movements in different parts of the yield curve (level, slope, and curvature) are connected to the behavior of Bitcoin’s price. Pyo and Lee ( 2020 ) explored the impact of the Federal Open Market Committee (FOMC) and macroeconomic announcements on the behavior of Bitcoin prices. They found that although Bitcoin responds to FOMC announcements, it does not react to macroeconomic announcements, such as the employment rate and the Consumer Price Index.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies

Aharon

Umar

2021

Financ Innov

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This study examines the connectedness between the US yield curve components (i.e., level, slope, and curvature), exchange rates, and the historical volatility of the exchange rates of the main safe-haven fiat currencies (Canada, Switzerland, EURO, Japan, and the UK) and the leading cryptocurrency, the Bitcoin. Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility. The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks. Meanwhile, the curvature of the yield curve and the Japanese Yen, Swiss Franc, and British Pound act mainly as net receivers. Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level, slope, and curvature, and from any main currency investigated. These findings hint that Bitcoin might provide hedging benefits. However, similar to the static analysis, our dynamic analysis shows that during different periods and particularly in stressful times, Bitcoin is far from being isolated from other currencies or the yield curve components. The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress. Evidence supporting this contention is the substantially increased connectedness due to policy shocks, political uncertainty, and systemic crisis, implying no empirical support for Bitcoin’s safe-haven property during stress times. The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times, Bitcoin has the property of a diversifier. The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.

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“…They have focused on confirming the influence of these events on cryptocurrency volatility. Pyo and Lee (2019) have analyzed the effects of macroeconomic and monetary policy announcements on Bitcoin prices. They justified these links by an event study approach.…”

Section: State Of the Artmentioning

confidence: 99%

“…Therefore, studying the volatility of these investments is vital when considering anomalies and bubbles in pricing behavior. Most studies have focused on these bubbles and anomalies out of the pandemic period (Corbet et al, 2020;Pyo and Lee, 2019) or without considering the FOMC announcement events (Fry, 2018). For this reason, this paper attempts to study the effects of the FOMC announcement on the cryptocurrency market and to assess if all cryptocurrencies react differently or they are all broadly comparable.…”

Section: State Of the Artmentioning

confidence: 99%

“…Their special form led several studies to focus on their efficiency and volatility (Corbet et al, 2020). As investment products, some studies consider Bitcoin as vulnerable to monetary and fiscal policies (Pyo and Lee, 2019) while others have found that it is immune to an FOMC announcement when it is considered as a protocol-based asset (Corbet et al, 2020). Criticisms of the role of monetary policy in the development of bubbles were notably advanced by Taylor (2009), who suggests that the low level of interest rates implemented by the Federal Reserve between 2001 and 2004 contributed to the emergence of a bubble in the US real estate market and, from there, to the boom in subprime loans and structured products.…”

Section: Introductionmentioning

confidence: 99%

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Cryptocurrency bubble risk and the FOMC announcements during COVID-19 black swan event

Jarboui¹,

Mnif

2021

JOIC

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Purpose After the COVID-19 outbreak, the Federal Reserve has undertaken several monetary policies to alleviate the pandemic consequences on the markets. This paper aims to evaluate the effects of the Federal Reserve monetary policy on the cryptocurrency dynamics during the COVID19 pandemic. Design/methodology/approach We examine the response and feedback effects via an event study methodology. For this purpose, abnormal returns (AR) and cumulative abnormal returns (CARs) around the first FOMC (Federal Open Market Committee) announcement related to the COVID-19 pandemic for the top five cryptocurrencies are explored. We, further investigate the effect of the eight FOMC statement announcements during the COVID19 pandemic on these cryptocurrencies (Bitcoin, Ethereum, Tether, Litecoin, and Ripple). In the above-mentioned crypto-currency markets, we investigate the presence of bubbles by using the PSY test. We then examine the concordance of the dates of these bubbles with the dates of the FOMC announcements. Findings The empirical results show that the first FOMC event has a negative significant effect after 4 days of the announcement date for all studied cryptocurrencies except Tether. The results also indicate that cumulative abnormal returns are significant during the event windows of (−3,8), (−3,9), and (−3,10). Besides, we find that Bitcoin, Ethereum and, Litecoin lived short bubbles lasting for a few days. However, Ripple and Tether markets present no bubbles and no explosive periods. Research limitations/implications This paper presents trained proof that FOMC announcements have a positive effect on volatility's predictive capacity. This work therefore promotes the study of the data quality of volatility in future research as well. Practical implications The justified effect of the FOMC announcements on cryptocurrency as a speculative asset has practical implications for investors in building their trading strategies in anticipation of the next FOMC announcement. Therefore, this study implies that the FOMC announcements contain very relevant information for investors in the cryptocurrency market. This research may not only encourage a better understanding of the evolution of the expectations of policymakers, but also facilitate a better understanding of how these expectations are developed. Originality/value The COVID-19 pandemic has disturbed the stability of financial markets, inciting the Fed to take some monetary regulations. To the best of our knowledge, this study is the first one that analyses the response of five major cryptocurrencies to FOMC announcements during COVID 19 pandemic and associates these dates with bubble occurrences.

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Do FOMC and macroeconomic announcements affect Bitcoin prices?

Cited by 40 publications

References 23 publications

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies

Cryptocurrency bubble risk and the FOMC announcements during COVID-19 black swan event

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