Modeling of Cryptocurrency Returns

"Modeling of Cryptocurrency Returns" is a captivating video that delves into the fascinating world of cryptocurrencies and explores the art of predicting and modeling their returns. In this insightful presentation, we dive into the various methodologies and techniques used to analyze historical data and develop models that can forecast the future performance of cryptocurrencies. From statistical models to machine learning algorithms, we uncover the secrets behind accurately estimating cryptocurrency returns. Whether you're a seasoned investor, a cryptocurrency enthusiast, or simply curious about the financial potential of these digital assets, this video offers valuable insights and strategies to help you navigate the complex world of cryptocurrency investments. Join us on this educational journey as we unlock the mysteries of cryptocurrency returns and empower you with the knowledge to make informed investment decisions in this dynamic and rapidly evolving market.Show more

Abstract
Purpose
Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Design/methodology/approach
Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

Findings
The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

Practical implications
One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

Originality/value
This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Citation
Malladi, R.K. (2023), "Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics", China Accounting and Finance Review, Vol. 25 No. 2, pp. 145-183. https://doi.org/10.1108/CAFR-02-2022-0001

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