On the Volatility and Market Inefficiency of Bitcoin During the COVID-19 Pandemic

The COVID-19 pandemic has been causing unprecedented economic downturn worldwide. As it wreaks havoc on every aspect of global economic activities, stakeholders are wondering how its impact can be quantified to craft viable responses. In the exotic field of cryptocurrencies, prior to the pandemic, everyone was excited about Bitcoin and its multitude of potentials. However, a day after COVID-19 was officially announced by the World Health Organization as a pandemic, the rate of return to Bitcoin dropped by an unheard-of one-day decline of-46.5%, and people started to rethink the prospects of Bitcoin. A day after this steep decline, Bitcoin recovered and started a sustained bull run which lasted for almost a year and even posted an all-time high daily uptick of 59.6%. By the end of July 2021, the price reached its all-time high but lost more than half of it at the end of the sample period. This study aims to empirically analyze the risk-return profile and the market efficiency of Bitcoin utilizing a 1,306-day data set conveniently subdivided into pre-pandemic and pandemic periods. The general conclusion of the study is: During the pandemic, Bitcoin is extremely volatile and does not subscribe to the efficient market hypothesis. © 2023 by De La Salle University.

Efficiency of the Cryptocurrency Market After the COVID-19 Pandemic

The aim of this study is to test the weak form of the efficient market hypothesis for the most highly capitalized cryptocurrencies in various categories (means of payment, payment systems, blockchain platforms, and utility tokens) after March 2020. The study uses standard statistical tools – autocorrelation tests and series tests – to evaluate the hypothesis, which is then tested in another way by attempting to arrive at profitable trading strategies using information from traditional markets to decide whether to open positions in various cryptocurrencies. The results of the statistical tests conducted demonstrate an increase in the efficiency of the cryptocurrency market and a consequent reduction in the profitability of speculative trading based on past prices (technical analysis). The trading simulations show that the increased sensitivity of the cryptocurrency market to the dynamics of traditional markets after March 2020 made it possible to find trading strategies that take into account market information and are able to generate significant excess returns compared to a "buy and hold” strategy during the downturn in the digital asset markets (2021–2022) for certain cryptocurrencies (Bitcoin, Dash, Zcash, Lumen). This supports the conclusion that the efficiency of the cryptocurrency market is gradually increasing in terms of identifying trading opportunities through technical analysis. However, the increase in comovement with the traditional market would suggest that it is inefficient to predict the returns of cryptocurrencies based on external information, which turned out to be irrelevant in other studies that examined earlier periods. © 2022,Ekonomicheskaya Politika.All Rights Reserved.

Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics

This paper investigates the impact of COVID-19 on financial markets. It focuses on the evolution of the market efficiency, using two efficiency indicators: the Hurst exponent and the memory parameter of a fractional Lévy-stable motion. The second approach combines, in the same model of dynamic, an alpha-stable distribution and a dependence structure between price returns. We provide a dynamic estimation method for the two efficiency indicators. This method introduces a free parameter, the discount factor, which we select so as to get the best alpha-stable density forecasts for observed price returns. The application to stock indices during the COVID-19 crisis shows a strong loss of efficiency for US indices. On the opposite, Asian and Australian indices seem less affected and the inefficiency of these markets during the COVID-19 crisis is even questionable. © 2022 Elsevier B.V.

High short interest stocks performance during the Covid-19 crisis: an informational efficacy measure based on permutation-entropy approach

Purpose: The author examine the performance of a number of high short interest stocks along with the prices of the GameStop stock and three major stock exchange indices, particularly for the period after the eruption of the Covid-19 crisis. Design/methodology/approach: With the employment of the complexity–entropy causality plane approach, the author categorize the stock prices in terms of the level of informational efficiency. Findings: The author reported that the efficiency level for the index of the high short interest stocks falls considerably, not only at the onset of the Covid-19 crisis but during the health crisis period at hand. This is translated into proof of less uncertainty in predicting the stock prices of these specific stocks. On the other hand, the GameStop prices exhibit the same behavior as those with the high short interest firms, but change considerably in the middle of the crisis. The reversal of the behavior, by obtaining higher informational efficiency levels, is attributed to the short squeeze frenzy that increased the price of the stock many times over. Among the stock market indices, the Dow Jones Industrial Average and the S&P 500 decreased their efficiency levels marginally, after the surge of the crisis, while the Russell 2000 index kept the level intact. The high and stable degree of randomness could be attributed to the measures taken concurrently by the Federal Reserve and the government immediately after the outbreak of the crisis. Originality/value: This is one of the few studies that examine the impact of short selling behavior on the efficiency level of certain stocks' prices, particularly during the health public crisis. It provides an alternative approach to measuring quantitatively the degree of inefficiency and randomness. © 2023, Emerald Publishing Limited.

Time-Varying Return Predictability and Adaptive Behavior in The U.S. Commodity Markets During COVID-19

The study investigates the time-varying efficiency of the four most commonly traded international commodities from the U.S. Chicago Board of Options Exchange (CBOE) over a more extended period as well as during COVID-19. The study also explores how adaptive behavior of returns induces profitable opportunities in the commodity markets. Daily returns of commodity indices (gold, silver, oil, metal) are divided into subsamples of six years, to apply a battery of linear/nonlinear tests. The study uncovers the linear and nonlinear serial dependence in returns from commodities and finds evidence of time-varying volatility, thus consistent with the Adaptive Market Hypothesis over the full sample period. Moreover, returns from all the commodities are highly volatile and predictable during COVID-19. JEL Classification: G4, G41 © International Journal of Economics and Management. ISSN 1823-836X. e-ISSN 2600-9390.

Testing Weak Form of Efficient Market Hypothesis before and during COVID-19 Pandemic Periods: Evidence from Indonesia

This research examines the Weak Form of Efficient Market Hypothesis (WFEMH) on the Indonesian Stock Exchange. Specifically, the study empirically tests the extent to which future stock price changes are not determined by the previous period's stock price movement or the stock price changes are random. Thus, future stock price changes fully reflect new relevant information on the market. This research utilizes the daily closing price of the Composite Stock Price Index on the Indonesian Stock Exchange from 2011 to 2021. The sample of the study is divided into two groups. The first group is from January 2011 to December 2019 as the normal pre-COVID-19 period, and the second group is from January 2020 to December 2021 as the economic crisis period (during COVID-19). We apply three statistical tests: A unit root test, serial correlation test, and regression model examining the WFEMH. The study found that the WFEMH is documented in the Indonesian Stock Exchange in some periods before and during COVID-19. These research findings advocate that regulators and policy-makers should monitor the issue of the market efficiency of public firms in Indonesia. © 2022 IEEE.

Weak-Form Efficiency of Shariah-compliant Finance Stocks: Evidence from KLSE of Malaysia

We examine the weak-form efficient market hypothesis (EMH) for the selected Shariah-compliant finance stocks from the Kuala Lumpur Stock Exchange (KLSE) from 1/17/92 to 8/26/2022. Based on the ADF and artificial neural network (ANN) unit root tests, our results suggest that all three Shariah-compliant stocks are efficient before the COVID-19 period. Our results of ANN test suggest that BURSA and BIMB are inefficient during the COVID-19 period. These results have some notable policy implications. © 2022 IEEE.

Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics

This paper investigates the impact of COVID-19 on financial markets. It focuses on the evolution of the market efficiency, using two efficiency indicators: the Hurst exponent and the memory parameter of a fractional Lévy-stable motion. The second approach combines, in the same model of dynamic, an alpha-stable distribution and a dependence structure between price returns. We provide a dynamic estimation method for the two efficiency indicators. This method introduces a free parameter, the discount factor, which we select so as to get the best alpha-stable density forecasts for observed price returns. The application to stock indices during the COVID-19 crisis shows a strong loss of efficiency for US indices. On the opposite, Asian and Australian indices seem less affected and the inefficiency of these markets during the COVID-19 crisis is even questionable.

Examining rational bubbles in global natural gas prices: Evidence from frequency domain estimates

Natural gas plays important role in the global energy sources, hence understanding its price behavior is important to various economic agents. This study examined whether rational bubbles existed in the three major natural gas markets by employing Fourier unit root tests and a nonparametric rank test for cointegration. Data comprises monthly data on the three largest natural gas indices in the world – Henry hub (USA), European and Asian (Japan) for the period from 2000M1–2021M12. The results reveal that the efficient market hypothesis holds in the market ruling out the possibility of arbitrage opportunities. The rank test estimates at 0.009677, 0.009872, and 0.009657 at 1% significant level for the pre-COVID-19 sample period, and at 0.009671, 0.09874, and 0.009661 at 1% significant level for the full sample period contradict the rational explosive bubble framework that suggests deviation of prices from the fundamentals. This suggests that natural gas prices markets are efficient at least in the weak form, the implication is that disruptions in the market will be short-lived as the market will fundamentally adjust back to equilibrium.

On the predictive power of CAPE or Shiller’s PE ratio: the case of the Greek stock market

This paper examines the capability of the Cyclically Adjusted Price to Earnings (CAPE) or Shiller’s P/E ratio, along with other relative valuation ratios such as the P/E and the P/BV, to predict future returns of the FTSE/ASE Large Cap Index, starting from the development of the index (1997) to December 2018. We have herein used several regression models in order to examine the relationship between the above ratios and the future returns of 1, 3, 5 and 10 years. We show that, while P/E and P/BV ratios are not correlated to future returns, the CAPE ratio and its variation CAPE 5, which uses real 5 year earnings, are efficient estimators of future returns. Our results imply the informational inefficiency of the Greek Stock Market.

Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?

This study proposes a new approach for testing for random walk behavior in daily Bitcoin returns (19/07/2010-03/03/2022) by contextualizing the Dickey-Fuller test in time-frequency space using continuous complex wavelet transforms. By splitting our full sample into smaller sub-sample periods segregated by Bitcoin halving dates, we find that Bitcoin returns are most predictable or least market efficient (i) at higher frequency or short-run cycles of between 2 and 16 days, (ii) between November-February months, (iii) during 'bubble' periods, (iv) across the consecutive halving dates, (v) during the 'Black Swan event' caused by financial market turmoil arising from the COVID-19 pandemic, and (vi) subsequent to the announcements of new COVID-19 variants. Altogether, our findings have important policy implications for different stakeholders in Bitcoin markets.

Pricing efficiency and asymmetric multifractality of major asset classes before and during COVID-19 crisis

We examine the impact of COVID-19 pandemic crisis on the pricing efficiency and asymmetric multifractality of major asset classes (S&P500, US Treasury bond, US dollar index, Bitcoin, Brent oil, and gold) within a dynamic framework. Applying permutation entropy on intraday data that covers between April 30, 2019 and May 13, 2020, we show that efficiency of all sample asset classes is deteriorated with the outbreak, and in most cases this deterioration is significant. Results are found to be robust under different analysis schemes. Brent oil is the highest efficient market before and during crisis. The degree of efficiency is heterogeneous among all markets. The analysis by an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach shows evidence of asymmetric multifractality in all markets which rise with the scales. The inefficiency is higher during downward trends before the pandemic crisis as well as during COVID-19 except for gold and Bitcoin. Moreover, the pandemic intensifies the inefficiency of all markets except Bitcoin. Findings reveal increased opportunities for price predictions and abnormal returns gains during the COVID-19 outbreak.

Changing efficiency of BRICS currency markets during the COVID-19 pandemic

This paper studies the information efficiency of BRICS currency markets during the COVID-19 pandemic using daily data spanning 3rd February 2020–31st August 2021. In our preliminary analysis, which consists of tests for random walk and martingale processes, we provide evidence of time-varying weak-form market efficiency in the currency markets. In our main empirical analysis, we use wavelet coherence tools to examine the time–frequency relationship between COVID-19 death rates and BRICS currency returns, and we find higher frequency components dominate periods of panic and financial turmoil. However, subsequent to government intervention in financial markets and the more recent rollout of mass vaccination programmes, we find that higher frequency oscillations disappear, and only very low-frequency co-movements remain. Important academic, market and policy implications derived from our study are discussed.

The implications of COVID-19: Bullwhip and ripple effects in global supply chains

Using the financial proxies in the efficient market hypothesis (EMH), we explore how the bullwhip effect (BWE) and ripple effect (RE) have been affected by the COVID-19 pandemic upstream and downstream. We developed two models to help us understand the phenomenon. The first model we created was the global airline stock index (GASI). This index represents all the global airline companies in the sample in one single node. We compared the GASI with each sample's buyer and supplier. The first result we observed was that some companies are more affected than others. We also found that the RE increased after the COVID-19 pandemic, but not the BWE. We then created a second model to analyze the difference in effects between companies. This reveals that the number of relationships in the supply chain network is positively related to the BWE and the RE. According to the regression analysis, the data also show that companies with a higher degree of operating level (DOL) and debts, and a lower share price, are the most vulnerable to the BWE and RE. We close by summarizing our findings and discussing future research opportunities.

The symmetric and asymmetric time-varying causality relationships between the covid-19 outbreak and the stock exchange: The case of selected countries

In this study, the effects of COVID-19 (mortality rate, case rate, and bed capacity) on the stock market was examined within the framework of the efficient market hypothesis. Unlike other studies in the literature, we used the variable of bed capacity besides the mortality rate and case rate variables. The relationship between the mentioned variables, using daily data between December 31 of 2019 and November 10 of 2020, has been analyzed with time-varying symmetric and asymmetric causality tests for China, Germany, the USA, and India. Considering that the responses to positive and negative shocks during the pandemic process may be different and that the results may change depending on time, time-varying symmetric and asymmetric causality tests were used. According to the time-varying symmetric causality test, stock markets in all countries were affected in the period when the cases first appeared. A causal relationship between COVID-19 and country stock markets was found. The results showed that the effects of the case rate and bed capacity on the stock market occurred around the same time in Germany and the United States;however, these dates differed in China and India. According to time-varying asymmetric causality test findings, the asymmetric effect of the pandemic on the stock market in countries emerged during the second wave. The findings showed that the period during which positive and negative information about the pandemic intensified coincided with the period during which the second wave occurred;besides, the results show the effect of this information on the stock market differed as positive and negative shocks. © Ekonomika 2021.

On equity market inefficiency during the COVID-19 pandemic.

We show that during the weeks following the initiation of the COVID-19 pandemic, the United States equity market was inefficient. This is demonstrated by showing that utility maximizing agents over the time period ranging from mid-February to late March 2020 can generate statistically significant profits by utilizing only historical price and virus related data to forecast future equity ETF returns. We generalize Merton's optimal portfolio problem using a novel method based upon a likelihood ratio in order to construct a dynamic trading strategy for utility maximizing agents. These strategies are shown to have statistically significant profitability and strong risk and performance statistics during the COVID-19 time-frame.