Testing of Random Walk Hypothesis in The Cryptocurrency Market: After Declaration of Global Pandemic

Authors

  • Mallesha. L.
  • Archana H. N.

DOI:

https://doi.org/10.33516/rb.v49i1.160-176p

Keywords:

Cryptocurrency, Efficient Market Hypothesis, R/S Hurst Exponent, Random Walk Hypothesis, VR test, Covid-19

Abstract

Due to recent developments, several countries have legalised cryptocurrencies, and large corporations have begun accepting them as a form of payment. One of the major concerns in this field is market efficiency, specifically regarding whether the cryptocurrency market follows the random walk hypothesis. To investigate this issue, analysed the top ten cryptocurrency prices spanning from March 11, 2020, to March 26, 2023, focusing on the period following the declaration of the global pandemic. The study employed several statistical validity tests, including the Ljung-Box test, runs test, Bartels’s test, variance ratio test, EGARCH (1,1) and R/S Hurst exponent. The study revealed that most cryptocurrencies do not conform to the random walk hypothesis, indicating that the market is inefficient. This study implies that investors may be able to earn abnormal profits through the use of arbitrage strategies in this young market.

Downloads

Download data is not yet available.

Downloads

Published

2024-03-23

How to Cite

L., M., & H. N., A. (2024). Testing of Random Walk Hypothesis in The Cryptocurrency Market: After Declaration of Global Pandemic. Research Bulletin, 49(1), 160–176. https://doi.org/10.33516/rb.v49i1.160-176p

Issue

Volume 49, Issue 1, April 2023

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

• Archana, H. N., Jayanna, S., & Hiremath, V. (2015). Impact of bond rating changes on stock prices in India: Rating agency wise analysis. Indian Journal of Research in Capital Markets, 2(4), 20–32.

• Aslan, A., & Sensoy, A. (2020). Intraday efficiency-frequency nexus in the cryptocurrency markets. Finance Research Letters, 35, 101298. https://doi.org/10.1016/j.frl.2019.09.013

• Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.

• Challa, M. L., Malepati, V., & Kolusu, S. N. R. (2020). S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA. Financial Innovation, 6(1), 47. https://doi.org/10.1186/s40854-020-00201-5

• Cheikh, N. B., Zaied, Y. B., & Chevallier, J. (2020). Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models. Finance Research Letters, 35, 101293. https://doi.org/10.1016/j.frl.2019.09.008

• Dol, M. (2021). Comparison of the GARCH, EGARCH, GJR-GARCH and TGARCH model in times of crisis for the S&P500, NASDAQ and Dow-Jones. Erasmus School of Economics.

• Dsouza, J. J., & Mallikarjunappa, T. (2015). Do the Stock Market Indices Follow Random Walk? Asia-Pacific Journal of Management Research and Innovation, 11(4), 251–273. https://doi.org/10.1177/2319510X15602969

• Fama, E. F. (1965). The Behavior of Stock-Market Prices. The Journal of Business, 38(1), 34–105.

• Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383. https://doi.org/10.2307/2325486

• Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259.

• Kalsie, A. (2012). Study of the Weak Form of Market Efficiency: An Empirical Study of Indian Stock Market. FIIB Business Review, 1(2), 37–45. https://doi.

org/10.1177/2455265820120208

• Kang, H.-J., Lee, S.-G., & Park, S.-Y. (2022). Information Efficiency in the Cryptocurrency Market:The Efficient-Market Hypothesis. Journal of Computer Information Systems, 62(3), 622–631. https://doi.org/10.1080/08874417.2021.1872046

• Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303.

• Lo, A. W., & MacKinlay, A. C. (1989). The size and power of the variance ratio test in finite samples: A Monte Carlo investigation. Journal of Econometrics, 40(2), 203–238.

• Mallesha, L., & Archana, H. N. (2023). Impact of Dividend Declarations on Stock Prices During COVID-19 in India: An Event Study Approach. Research Bulletin, 48(3–4), 71–87.

• Markowitz, H. (1952). The utility of wealth. Journal of Political Economy, 60(2), 151–158.

• Nalın, H. T., & Güler, S. (2015). Testing The Random Walk Hypothesis: An Application in the BRIC Countries and Turkey. 55, 21.

• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347–370.

• Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Review, 21(3), 256–268. https://doi.org/10.1016/j. bir.2020.10.006

• Pereira da Silva, P. (2022). Market efficiency and the capacity of stock prices to track a firm’s future profitability. Borsa Istanbul Review, 22(3), 452–464. https://doi.org/10.1016/j. bir.2021.06.010

• Roll, R., & Ross, S. A. (1980). An empirical investigation of the arbitrage pricing theory. The Journal of Finance, 35(5), 1073–1103.

• Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.

• Tuyon, J., & Ahmad, Z. (2016). Behavioural finance perspectives on Malaysian stock market efficiency. Borsa Istanbul Review, 16(1), 43–61. https://doi.org/10.1016/j.bir.2016.01.001

• Verma, R., Sharma, D., & Sam, S. (2022). Testing of Random Walk Hypothesis in the Cryptocurrency Market. FIIB Business Review, 23197145221101240.

• Wald, A., & Wolfowitz, J. (1940). On a Test Whether Two Samples are from the Same Population. The Annals of Mathematical Statistics, 11(2), 147–162. https://doi.org/10.1214/aoms/1177731909