Measuring conditional correlation between financial markets' inefficiency

Fabrizio Di Sciorio; Raffaele Mattera; Juan Evangelista Trinidad Segovia; Fabrizio Di Sciorio; Raffaele Mattera; Juan Evangelista Trinidad Segovia

doi:10.3934/QFE.2023025

Quantitative Finance and Economics

2023, Volume 7, Issue 3: 491-507. doi: 10.3934/QFE.2023025

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Research article

Measuring conditional correlation between financial markets' inefficiency

Fabrizio Di Sciorio ¹,
Raffaele Mattera ²,
Juan Evangelista Trinidad Segovia ^{1
,
,}

1.
Department of Business and Economics, University of Almeria, Spain
2.
Department of Social and Economic Sciences, Sapienza University of Rome, Italy

Received: 05 June 2023 Revised: 21 August 2023 Accepted: 21 September 2023 Published: 27 September 2023
JEL Codes: G15, G170, C58

Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ($ h_t $). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemic and the Russo-Ukranian war. To empirically validate the analysis, we compared different stock markets in terms of conditional and unconditional correlations of dynamic inefficiency and investigated the predicted power of inefficiency measures through the Granger causality test.
- Hurst exponent,
- efficient market hypothesis,
- dynamic conditional correlation,
- multivariate GARCH,
- Hurst-based GARCH
Citation: Fabrizio Di Sciorio, Raffaele Mattera, Juan Evangelista Trinidad Segovia. Measuring conditional correlation between financial markets' inefficiency[J]. Quantitative Finance and Economics, 2023, 7(3): 491-507. doi: 10.3934/QFE.2023025

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Abstract

Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ($ h_t $). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemic and the Russo-Ukranian war. To empirically validate the analysis, we compared different stock markets in terms of conditional and unconditional correlations of dynamic inefficiency and investigated the predicted power of inefficiency measures through the Granger causality test.

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