An Investigation into the Dependence Structure of Major Cryptocurrencies

Saha, Kunal

doi:10.2139/ssrn.3241216

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…A large number of studies have demonstrated that complex socio-economic systems (Máté and Néda 2016), more specifically stocks (Merton 1973;1971;Black and Scholes 1973;Wilmott et al 1995;Bouchaud and Potters 2000;Mantegna and Stanly 2000;Reddy and Clinton 2016;Øksendal 2003), commodities (Schwartz 1997;Mejía Vega 2018), and cryptocurrencies (Cretarola and Figà-Talamanca 2019b; can be modeled using SDEs. Further, correlations do exist between some stocks (Mantegna and Stanly 2000;Teng et al 2016;Plerou et al 2002;Sándor and Néda 2015;Onnela et al 2003), various cryptocurrencies (Saha 2018;Chaim and Laurini 2019), and possibly exist between online social media activities. We exploit these correlations and construct a general predictive method for sets of cryptocurrency markets.…”

Section: Introductionmentioning

confidence: 99%

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

et al. 2020

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The growing interconnectivity of socioeconomic systems requires one to treat multiple relevant social and economic variables simultaneously as parts of a strongly interacting complex system. Here, we analyze and exploit correlations between the price fluctuations of selected cryptocurrencies and social media activities, and develop a predictive framework using noise-correlated stochastic differential equations. We employ the standard Geometric Brownian Motion to model cryptocurrency rates, while for social media activities and trading volume of cryptocurrencies we use the Geometric Ornstein-Uhlenbeck process. In our model, correlations between the different stochastic variables are introduced through the noise in the respective stochastic differential equation. Using a Maximum Likelihood Estimation on historical data of the corresponding cryptocurrencies and social media activities we estimate parameters, and using the observed correlations, forecast selected time series. We successfully analyze and predict cryptocurrency related social media and the cryptocurrency market itself with a reasonable degree of accuracy. In particular, we show that our method has impressive accuracy in predicting whether a cryptocurrency market will increase or decrease a day in the future, a significant result with regards to investing and trading cryptocurrencies.

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Section: Introductionmentioning

confidence: 99%

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

et al. 2020

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Cryptocurrency portfolio optimization: Utilizing a GARCH‐copula model within the Markowitz framework

Jeleskovic,

Latini,

Younas

et al. 2024

J Corp Accounting Finance

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The growing interest in cryptocurrencies has brought this new means of exchange to the attention of the financial world. This study aims to investigate the effects that a cryptocurrency can have when it is considered as a financial asset. The analysis is carried out from an ex‐post perspective, evaluating the performance achieved in a certain period by three different portfolios. These are the one composed only of equities, bonds and commodities, the second one only of cryptocurrencies, and the third one is a combination of these both ones and thus made up of all considered “traditional” assets and the most performing cryptocurrency of the second portfolio. For these purposes, the classic variance‐covariance approach is applied where the calculation of the risk structure is done via the GARCH‐Copula and GARCH‐Vine Copula approaches. The optimal weights of the assets in the optimized portfolios are determined through Markowitz optimization problem. The analysis mainly showed that the portfolio composed of cryptocurrency and traditional assets has a higher Sharpe index, from an ex‐post perspective, and more stable performances, from an ex‐ante perspective. We justify our selection of the Markowitz approach over conditional VaR and expected shortfall due to their heightened sensitivity to unsystematic extreme events in crypto markets.

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An Investigation into the Dependence Structure of Major Cryptocurrencies

Cited by 2 publications

References 28 publications

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

Cryptocurrency portfolio optimization: Utilizing a GARCH‐copula model within the Markowitz framework

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