The Impact of COVID-19 on Weak-Form Efficiency in Cryptocurrency and Forex Markets

Zitis, Pavlos I.; Kakinaka, Shinji; Umeno, Ken; Stavrinides, Stavros G.; Hanias, Michael P.; Potirakis, Stelios M.

doi:10.3390/e25121622

Cited by 2 publications

(1 citation statement)

References 64 publications

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“…The application scope of DFA and DCCA methods has been extended to the multifractal dimension. Multifractal detrended fluctuation analysis (MFDFA) [12] can detect whether there is a multifractal phenomenon in the time series, and it has been proven that there is a certain inverse relationship between the efficiency of the financial market and multifractality [13][14][15]. The multifractal detrended cross-correlation analysis (MFDCCA) [16] is used to demonstrate multifractal behavior in power-law cross-correlations between series, and there are numerous examples of its effective application, e.g., ref.…”

Section: Introductionmentioning

confidence: 99%

Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates

Yang,

Wang,

et al. 2024

Entropy

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Correlations between exchange rates are valuable for illuminating the dynamics of international trade and the financial dynamics of countries. This paper explores the changing interactions of the US foreign exchange market based on detrended cross-correlation analysis. First, we propose an objective way to choose a time scale parameter appropriate for comparing different samples by maximizing the summed magnitude of all DCCA coefficients. We then build weighted signed networks under this optimized time scale, which can clearly display the complex relationships between different exchange rates. Our study shows negative cross-correlations have become pyramidally rare in the past three decades. Both the number and strength of positive cross-correlations have grown, paralleling the increase in global interconnectivity. The balanced strong triads are identified subsequently after the network centrality analysis. Generally, while the strong development links revealed by foreign exchange have begun to spread to Asia since 2010, Europe is still the center of world finance, with the euro and Danish krone consistently maintaining the closest balanced development relationship. Finally, we propose a fluctuation propagation algorithm to investigate the propagation pattern of fluctuations in the inferred exchange rate networks. The results show that, over time, fluctuation propagation patterns have become simpler and more predictable.

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Section: Introductionmentioning

confidence: 99%

Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates

Yang,

Wang,

et al. 2024

Entropy

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Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures

Zitis,

Potirakis,

Alexandridis

2024

JRFM

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In this article, we examine whether incorporating complexity measures as features in deep learning (DL) algorithms enhances their accuracy in predicting forex market volatility. Our approach involved the gradual integration of complexity measures alongside traditional features to determine whether their inclusion would provide additional information that improved the model’s predictive accuracy. For our analyses, we employed recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) as DL model architectures, while using the Hurst exponent and fuzzy entropy as complexity measures. All analyses were conducted on intraday data from four highly liquid currency pairs, with volatility estimated using the Range-Based estimator. Our findings indicated that the inclusion of complexity measures as features significantly enhanced the accuracy of DL models in predicting volatility. In achieving this, we contribute to a relatively unexplored area of research, as this is the first instance of such an approach being applied to the prediction of forex market volatility. Additionally, we conducted a comparative analysis of the three models’ performance, revealing that the LSTM and GRU models consistently demonstrated a superior accuracy. Finally, our findings also have practical implications, as they may assist risk managers and policymakers in forecasting volatility in the forex market.

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The Impact of COVID-19 on Weak-Form Efficiency in Cryptocurrency and Forex Markets

Cited by 2 publications

References 64 publications

Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates

Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates

Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures

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