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Interest in cryptocurrencies predicts conditional correlation dynamics
Finance Research Letters, 2022Abstract Using a Smooth Transition Conditional Correlation model with Google search data as a transition variable, I investigate correlation dynamics between a set of crypto-currencies. A major change in the correlation dynamics after the 2017 bubble burst is explained by the attention subsequently surrounding cryptocurrencies.
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, 2019
This study examines the time-varying correlations between six cryptocurrency and S&P 500 index markets using a copula-ADCC-EGARCH model. The increasing influence and usage of cryptocurrencies has led the notion in which it is regarded as risky assets. In
A. Tiwari, Ibrahim D. Raheem, S. Kang
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This study examines the time-varying correlations between six cryptocurrency and S&P 500 index markets using a copula-ADCC-EGARCH model. The increasing influence and usage of cryptocurrencies has led the notion in which it is regarded as risky assets. In
A. Tiwari, Ibrahim D. Raheem, S. Kang
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Dynamic conditional relationships between developed and emerging markets
Physica A: Statistical Mechanics and its Applications, 2018This study examines the dynamic conditional correlations between the US and Korean financial markets and identifies the determinants of those correlations using the VAR-DCC-MGARCH model.
Wonho Song, Sung Y. Park, Doojin Ryu
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Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns
, 2019A non-Gaussian multivariate regime switching dynamic correlation model for financial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns.
Marc S. Paolella +2 more
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Volatility Threshold Dynamic Conditional Correlations: An International Analysis [PDF]
Journal of Financial Econometrics, 2012 This article proposes a modeling framework for the study of changes in cross-market comovement conditional on volatility regimes. Methodologically, we extend the Dynamic Conditional Correlation multivariate GARCH model to allow the dynamics of correlations to depend on asset variances through a threshold structure.
M. Kasch, CAPORIN, MASSIMILIANO
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The North American journal of economics and finance, 2021
Q. Pan, Xiaowen Mei, Tianqin Gao
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Q. Pan, Xiaowen Mei, Tianqin Gao
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Dynamic Conditioning and Credit Correlation Baskets [PDF]
, 2008 Dynamic conditioning is a technique that allows one to formulate correlation models for large baskets without incurring in the curse of dimensionality. The individual price processes for each reference name can be described by a lattice model specified semi-parametrically or even nonparametrically and which can realistically have about 1000 sites.
Albanese, Claudio, Vidler, Alicia
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Forecasting Time-Varying Correlation using the Dynamic Conditional Correlation (DCC) Model [PDF]
, 2014 Hedging strategies have become more and more complicated as assets being traded have become more interrelated to each other. Thus, the estimation of risks for optimal hedging does not involve only the quantification of individual volatilities but also include their pairwise correlations.
Mapa, Dennis S. +3 more
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Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation
Applied Financial Economics Letters, 2006This paper introduces the Flexible Dynamic Conditional Correlation (FDCC) multivariate GARCH model which generalizes the Dynamic Conditional Correlation (DCC) multivariate GARCH model proposed by Engle (2002). The FDCC model relax the assumption of common dynamics among all assets used in the DCC model.
M. BILLIO +2 more
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Tracking Dynamic Conditional Neural Correlation during Task Learning
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Single neuron modulates the external stimuli, and neural population coordinates to encode information. An alternate method for examining the coordinated populational activity in neural encoding is conditional neural correlation (CNC). However, such correlations are not static during a new task learning process as neurons adapt their tunings over time ...
Zixu, Wang +3 more
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