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Volatility and dynamic dependence modeling: Review, applications, and financial risk management

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Abstract Since the introduction of ARCH models close to 40 years ago, a wide range of models for volatility estimation and prediction have been developed and integrated into asset allocation, financial derivative pricing, and financial risk management. Research has also been very active in extending volatility modeling to dependence modeling and in developing our understanding of risk and uncertainty in financial systems. This paper presents a review on the statistical modeling on volatility and dynamic dependence of financial returns. In addition, we present a real data example using a time‐varying copula model to estimate the dynamic dependence of stock returns. Research on volatility and dynamic dependence modeling will continue to encounter statistical and computational challenges; it is necessary to persist in dealing with the 3H (high dimension, high frequency, high complexity) paradigm in modeling. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Models > Nonlinear Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Moving‐window 20‐day time‐varying volatility and dynamic correlations of GOOGL, BAC and KO from 26 December 2017 to 31 December 2020
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Dynamic conditional dependence for and using vine copula GARCH model
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Canonical vine and D‐vine for
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D‐vine with four variables
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Canonical vine with four variables
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Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Statistical Models > Nonlinear Models
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

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