East Asian Economic Review

Korea Institute for International Economic Policy (대외경제정책연구원)
권호 표지
DOI QR코드

DOI QR Code

In-Sample and Out-of-Sample Predictability of Cryptocurrency Returns

  • Received : 2023.08.06
  • Accepted : 2023.09.18
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

Keywords

References

  1. Aldous, D. 1989. Probability Approximations via the Poisson Clumping Heuristic. Springer Science & Business Media.
  2. Ashley, R., Granger, C. W. J. and R. Schmalensee. 1980. "Advertising and Aggregate Consumption: An analysis of Causality." Econometrica, vol. 48, no. 5, pp. 1149-1167. https://doi.org/10.2307/1912176
  3. Baur, D. G., Hong, K. and A. D. Lee. 2018. "Bitcoin: Medium of Exchange or Speculative Assets?" Journal of International Financial Markets, Institutions and Money, vol. 54, pp. 177-189. https://doi.org/10.1016/j.intfin.2017.12.004
  4. Bouoiyour, J. and R. Selmi. 2015. "What Does BitCoin Look Like?" Annals of Economics and Finance, vol. 16, no. 2, pp. 449-492.
  5. Buchholz, M., Delaney, J., Warren, J. and J. Parker. 2012. "Bits and Bets, Information, Price Volatility, and Demand for BitCoin." https://www.reed.edu/economics/parker/s12/312/finalproj/Bitcoin.pdf
  6. Cheah, E. T. and J. Fry. 2015. "Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into the Fundamental Value of Bitcoin." Economics Letters, vol. 130, pp. 32-36. https://doi.org/10.1016/j.econlet.2015.02.029
  7. Ciaian, P., Rajcaniova, M. and d'Artis Kancs. 2016. "The Economics of Bitcoin Price Formation." Applied Economics, vol. 48, no. 19. pp. 1799-1815. https://doi.org/10.1080/00036846.2015.1109038
  8. Clark, T. E. and M. W. McCracken. 2001. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models." Journal of Econometrics, vol. 105, no. 1, pp. 85-110. https://doi.org/10.1016/S0304-4076(01)00071-9
  9. Dyhrberg, A. H. 2016. "Bitcoin, Gold and the Dollar-A GARCH volatility analysis." Finance Research Letters, vol. 16, pp. 85-92. https://doi.org/10.1016/j.frl.2015.10.008
  10. Foster, F. D., Smith, T. and R. E. Whaley. 1997. "Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R2." Journal of Finance, vol. 53, no. 2, pp. 591-607. https://doi.org/10.2307/2329491
  11. Hanley, B. P. 2013. "The False Premises and Promises of Bitcoin." https://doi.org/10. 48550/arXiv.1312.2048
  12. Inoue, A. and L. Kilian. 2004. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?" Econometric Reviews, vol. 23, no. 4, pp. 371-402. https://doi.org/10.1081/ETC-200040785
  13. Kristoufek, L. 2013. "Bitcoin Meets Google Trends and Wikipedia: Quantifying the Relationship between Phenomena of the Internet Era." Scientific Reports, vol. 3, pp. 1-7. https://doi.org/10.1038/srep03415
  14. Kristoufek, L. and E. Scalas. 2015. "What are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis." PLoS ONE, vol. 10, no. 4. https://doi.org/10.1371/journal.pone.0123923
  15. Lo, A. W. and A. C. MacKinlay. 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models." Review of Financial Studies, vol. 3, no. 3, pp. 431-467. https://doi.org/10.1093/rfs/3.3.431
  16. McCracken, M. W. 2007. "Asymptotics for Out-of-Sample Tests of Granger Causality." Journal of Econometrics, vol. 140, no. 2, pp. 719-752. https://doi.org/10.1016/j.jeconom.2006.07.020
  17. Rapach, D. E. and M. E. Wohar. 2006. "In-Sample vs. Out-of-Sample Tests of Stock Return Predictability in the Context of Data Mining." Journal of Empirical Finance, vol. 13, no. 2, pp. 231-247. https://doi.org/10.1016/j.jempfin.2005.08.001
  18. Selgin, G. 2015. "Synthetic Commodity Money." Journal of Financial Stability, vol. 17, pp. 92-99. https://doi.org/10.1016/j.jfs.2014.07.002
  19. van Wijk, D. 2013. "What Can Be Expected from the BitCoin?" Bachelor's Thesis, Erasmus University. http://hdl.handle.net/2105/14100
  20. Yermack, D. 2013. "Is Bitcoin a Real Currency? An Economic Appraisal." NBER Working Papers, no. 19747. National Bureau of Economic Research.