Copulas for Modeling the Relationship Between Inflation and the Exchange Rate

Hassou, Laila Ait; Badaoui, Fadoua; Cyrille, Okou Guei; Amar, Amine; Zoglat, Abdelhak; Ezzahid, Elhadj

doi:10.1007/978-3-319-96944-2_15

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…Hence, the exchange rate can be said to be linked to inflation rate volatility through importation of goods and materials needed for production. e dependency of macroeconomic indicators has been shown in several papers to have some existing relationship [1][2][3]. However, this relationship has been unveiled using different approaches and methods.…”

Section: Introductionmentioning

confidence: 99%

“…Our work is similar but different from [3] which also used copula to model the relationship between inflation and exchange rate using data from the European Central Bank between the period 2000 and 2016. e authors did this by using SARIMA to obtain the margins for the copula. However, our data were best fitted by GARCH(1,1) after which we fitted marginal distributions from the residuals and then obtained the uniform margins from these univariate distributions for copula estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Ait Hassou et al [3] used copula to model the dependency between inflation and exchange rate using data from European banks. Our study differs from this study in three ways:…”

Section: Introductionmentioning

confidence: 99%

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Modelling the Dependency between Inflation and Exchange Rate Using Copula

Kwofie

Akoto

Opoku-Ameyaw

2020

Journal of Probability and Statistics

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In this paper, we propose a copula approach in measuring the dependency between inflation and exchange rate. In unveiling this dependency, we first estimated the best GARCH model for the two variables. Then, we derived the marginal distributions of the standardised residuals from the GARCH. The Laplace and generalised t distributions best modelled the residuals of the GARCH(1,1) models, respectively, for inflation and exchange rate. These marginals were then used to transform the standardised residuals into uniform random variables on a unit interval [0, 1] for estimating the copulas. Our results show that the dependency between inflation and exchange rate in Ghana is approximately 7%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling the Dependency between Inflation and Exchange Rate Using Copula

Kwofie

Akoto

Opoku-Ameyaw

2020

Journal of Probability and Statistics

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“…Two researchers [11] and [12] used copulas to model the relationship between inflation and exchange rate. When modeling the relationship between inflation and exchange rates, [12] used data from European banks between 2000 and 2016; [11] used Ghanaian data between 2000 and 2018. Our research departs from their research in three ways.…”

Section: Introductionmentioning

confidence: 99%

“…For estimating the marginal distribution, we employed the ARMA + GARCH model as opposed to [11] who used GARCH. [11] used GARCH because it is well known to capture the volatility and [12] used SARIMA because their data exhibited the presence of seasonality.…”

Section: Introductionmentioning

confidence: 99%

A Copula-Based Approach for Modelling the Dependence between Inflation and Exchange Rate in Kenya

Tracy

Waititu

Sewe

2022

AJPAS

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In this study, we modeled the dependence structure between inflation and exchange rate using the copula approach. To formulate a bivariate copula, we used ARMA+GARCH to model serial dependence for each univariate series of returns. Both for in ation and exchange rate, it was found that the student t distribution was the best marginal distribution. Then, we transformed the standardized residuals from those marginal distributions (student t) into uniform over the range [0; 1]. To estimate the copula, we used a parametric approach. Gumbel copula was found to be the best to capture the dependence. We investigated the time-varying dependence using change-point detection based on copula. We found that there is a significant change in the nature of dependence over the period under consideration. The change in the nature of dependence between the two variables was in line with the prevailing macro-economics conditions during the period under review. We recommend to the future researchers to consider studying time-varying dependence between those two variables and investigate also the change in copula parameters in values with time. We also recommend including other macroeconomic variables while modeling the relationship between in ation and exchange rate.

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