Market risk and Bitcoin returns

Koutmos, Dimitrios

doi:10.1007/s10479-019-03255-6

Cited by 60 publications

(35 citation statements)

References 31 publications

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“…(2015 ) observed a similar relationship between Bitcoin returns and volatility and concluded that the addition of Bitcoin in the asset portfolio might help investors to achieve the diversification benefits. When dividing the returns into high and low price volatility regimes, Koutmos (2019 ) observed that a higher volatility regime is associated with higher mean returns. However, he also observed that the returns in the high volatility regime do not always reward the investors for the higher levels of volatility, and are prone to tail risks when compared with the low volatility regimes.…”

Section: Resultsmentioning

confidence: 99%

Portfolio optimization in the era of digital financialization using cryptocurrencies

Ahmad

Liu

et al. 2020

Technological Forecasting and Social Change

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Section: Resultsmentioning

confidence: 99%

Portfolio optimization in the era of digital financialization using cryptocurrencies

Ahmad

Liu

et al. 2020

Technological Forecasting and Social Change

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“…Cryptocurrencies are well known for their hedging, portfolio diversification, and risk mitigation capabilities across the literature (Bouoiyour et al 2019 ; Bouri et al 2018 ; Koutmos 2019 ). Bitcoin, the most popular cryptocurrency, has drawn crucial market players' attention at different hierarchies for accomplishing these activities.…”

Section: Introductionmentioning

confidence: 99%

A differential evolution-based regression framework for forecasting Bitcoin price

2021

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This research proposes a differential evolution-based regression framework for forecasting one day ahead price of Bitcoin. The maximal overlap discrete wavelet transformation first decomposes the original series into granular linear and nonlinear components. We then fit polynomial regression with interaction (PRI) and support vector regression (SVR) on linear and nonlinear components and obtain component-wise projections. The sum of these projections constitutes the final forecast. For accurate predictions, the PRI coefficients and tuning of the hyperparameters of SVR must be precisely estimated. Differential evolution, a metaheuristic optimization technique, helps to achieve these goals. We compare the forecast accuracy of the proposed regression framework with six advanced predictive modeling algorithms-multilayer perceptron neural network, random forest, adaptive neural fuzzy inference system, standalone SVR, multiple adaptive regression spline, and least absolute shrinkage and selection operator. Finally, we perform the numerical experimentation based on-(1) the daily closing prices of Bitcoin for January 10, 2013, to February 23, 2019, and (2) randomly generated surrogate time series through Monte Carlo analysis. The forecast accuracy of the proposed framework is higher than the other predictive modeling algorithms.

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“…For example, see El Alaoui et al (2019) andBouri et al (2018a, b, c). For other various aspects of the cryptocurrency markets, seeCretarola and Figà-Talamanca (2019),Giudici and Polinesi (2019) andKoutmos (2019).…”

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confidence: 99%

Prediction of cryptocurrency returns using machine learning

2020

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In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55-65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.

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Market risk and Bitcoin returns

Cited by 60 publications

References 31 publications

Portfolio optimization in the era of digital financialization using cryptocurrencies

Portfolio optimization in the era of digital financialization using cryptocurrencies

A differential evolution-based regression framework for forecasting Bitcoin price

Prediction of cryptocurrency returns using machine learning

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