In-Sample and Out-of-Sample Predictability of Cryptocurrency Returns
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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.
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.
참고문헌 (20)
- Aldous, D, "Probability Approximations via the Poisson Clumping Heuristic" (1989), Springer Science & Business Media
- Ashley, R., "Advertising and Aggregate Consumption: An analysis of Causality", Econometrica, 48(5), pp.1149~1167 (1980)
- Dirk G. Baur, "Bitcoin: Medium of exchange or speculative assets?", Journal of International Financial Markets, Institutions and Money, 54, pp.177~189 (2018), Elsevier BV[DOI]
- Bouoiyour, J., "What Does BitCoin Look Like?", Annals of Economics and Finance, 16(2), pp.449~492 (2015)
- "Bits and Bets, Information, Price Volatility, and Demand for BitCoin" (2012)[URL]
- Eng-Tuck Cheah, "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin", Economics Letters, 130, pp.32~36 (2015), Elsevier BV[DOI]
- Pavel Ciaian, "The economics of BitCoin price formation", Applied Economics, 48(19), pp.1799~1815 (2015), Informa UK Limited[DOI]
- Clark, T. E., "Tests of Equal Forecast Accuracy and Encompassing for Nested Models", Journal of Econometrics, 105(1), pp.85~110 (2001)
- Anne Haubo Dyhrberg, "Bitcoin, gold and the dollar – A GARCH volatility analysis", Finance Research Letters, 16, pp.85~92 (2016), Elsevier BV[DOI]
- F. Douglas Foster, "Assessing Goodness-Of-Fit of Asset Pricing Models: The Distribution of the Maximal R 2", The Journal of Finance, 52(2), pp.591~ (1997), JSTOR[DOI]
- "The False Premises and Promises of Bitcoin" (2013)[URL]
- Atsushi Inoue, "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?", Econometric Reviews, 23(4), pp.371~402 (2005), Informa UK Limited[DOI]
- Ladislav Kristoufek, "BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era", Scientific Reports, 3(1) (2013), Springer Science and Business Media LLC[DOI]
- Ladislav Kristoufek, "What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis", PLOS ONE, 10(4), pp.e0123923~ (2015), Public Library of Science (PLoS)[DOI]
- Lo, A. W., "Data-Snooping Biases in Tests of Financial Asset Pricing Models", Review of Financial Studies, 3(3), pp.431~467 (1990)
- McCracken, M. W, "Asymptotics for Out-of-Sample Tests of Granger Causality", Journal of Econometrics, 140(2), pp.719~752 (2007)
- Rapach, D. E., "In-Sample vs. Out-of-Sample Tests of Stock Return Predictability in the Context of Data Mining", Journal of Empirical Finance, 13(2), pp.231~247 (2006)
- Selgin, G, "Synthetic Commodity Money", Journal of Financial Stability, 17, pp.92~99 (2015)
- van Wijk, D., "What Can Be Expected from the BitCoin?", pp.2013
- Is Bitcoin a Real Currency? An Economic Appraisal, "Yermack, D", National Bureau of Economic Research, ~, pp.NBER Working Papers, no. 19747 (2013)