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Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar

Author

Listed:
  • Astrid Ayala

    (Universidad Francisco Marroquín)

  • Szabolcs Blazsek

    (Universidad Francisco Marroquín)

Abstract

In this paper we introduce new Dynamic Conditional Score (DCS) models for the Skew-Gen-t (Skewed Generalized t) and NIG (Normal-Inverse Gaussian) distributions as alternatives to the recent DCS models for the Student’s-t and EGB2 (Exponential Generalized Beta of the second kind) distributions, respectively. The DCS models we propose include stochastic local level, stochastic seasonality, and irregular components with DCS-EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) volatility dynamics. DCS models are robust to extreme observations, whereas standard financial time series models are not. We use data from the Guatemalan Quetzal (GTQ) to United States Dollar (USD) exchange rate for the period of 4th January 1994–30th June 2017. This dataset exhibits significant rises and falls in the GTQ/USD that lead to extreme observations, stochastic seasonality with dynamic amplitude, and volatility dynamics. These seasonality dynamics of the GTQ/USD are related to the Guatemalan trade-related currency movements, receipt and payment of foreign loans, and remittance payments of Guatemalans working abroad. We show that the in-sample statistical performance of the DCS-Skew-Gen-t and the DCS-NIG models is superior to that of the DCS-t and the DCS-EGB2 models, respectively. Furthermore, we show that the statistical performance of all DCS models is superior to that of the standard financial time series model.

Suggested Citation

  • Astrid Ayala & Szabolcs Blazsek, 2019. "Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(1), pages 65-92, March.
    • Handle: RePEc:spr:series:v:10:y:2019:i:1:d:10.1007_s13209-018-0186-0
    DOI: 10.1007/s13209-018-0186-0
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    References listed on IDEAS

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    Cited by:

    1. Song, Shijia & Li, Handong, 2022. "Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2024. "Noising the GARCH volatility: A random coefficient GARCH model," MPRA Paper 120456, University Library of Munich, Germany, revised 15 Mar 2024.
    3. Ayala, Astrid & Blazsek, Szabolcs, 2019. "Score-driven time series models with dynamic shape : an application to the Standard & Poor's 500 index," UC3M Working papers. Economics 28133, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Blazsek, Szabolcs & Licht, Adrian, 2019. "Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production," UC3M Working papers. Economics 29030, Universidad Carlos III de Madrid. Departamento de Economía.

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    More about this item

    Keywords

    Dynamic Conditional Score (DCS) models; Guatemalan Quetzal (GTQ) to United States Dollar (USD) exchange rate; Stochastic seasonality component;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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