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Measuring dynamic dependency using time-varying copulas with extended parameters: Evidence from exchange rates data.

Atina Ahdika1,2, Dedi Rosadi1, Adhitya Ronnie Effendie1

  • 1Department of Mathematics, Universitas Gadjah Mada, Yogyakarta, Indonesia.

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Summary
This summary is machine-generated.

This study introduces a new method to analyze exchange rate dynamics using extended time-varying copulas, revealing significant pandemic impacts on Asian economies. The approach effectively models and forecasts currency exchange rate dependencies during crises.

Keywords:
ARIMA-GARCHDynamic parameterExchange ratesForcing variableTime-varying copulas

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Area of Science:

  • Econometrics
  • Financial Mathematics
  • Time Series Analysis

Background:

  • Exchange rate dynamics are crucial for global financial stability.
  • Understanding interdependencies is vital, especially during economic crises like the 2020 pandemic.
  • Existing models may not fully capture the evolving nature of these dependencies.

Purpose of the Study:

  • To propose a novel approach for investigating dynamic dependencies among exchange rates.
  • To extend time-varying copulas with autoregressive moving average (ARMA) processes for enhanced modeling.
  • To apply the model to major Asian economies' exchange rates before and during the COVID-19 pandemic.

Main Methods:

  • Utilized an autoregressive moving average (ARMA) process to extend time-varying copulas parameters.
  • Employed the ARIMA-GARCH model for exchange rate data modeling.
  • Applied extended time-varying copulas to daily exchange rate data of five Asian countries (CNY/USD, IDR/USD, INR/USD, JPY/USD, KRW/USD).

Main Results:

  • Confirmed dynamic dependencies among the exchange rates of China and four other Asian countries, both pre- and during the pandemic.
  • Identified the Indian Rupee (INR/USD) as the most significantly affected exchange rate by the pandemic.
  • Demonstrated the effectiveness of the proposed algorithms in analyzing and forecasting exchange rate data during crisis periods.

Conclusions:

  • The extended time-varying copula approach effectively captures dynamic dependencies in exchange rates during crises.
  • The COVID-19 pandemic induced significant shifts in exchange rate relationships among major Asian economies.
  • The proposed methodology offers a computationally accessible tool for financial risk management and forecasting.