New Hybrid Fuzzy Time Series Model: Forecasting the foreign exchange market

José Eduardo Medina Reyes; Salvador Cruz Aké; Agustín Ignacio Cabrera Llanos

doi:10.22201/fca.24488410e.2021.2623

New Hybrid Fuzzy Time Series Model: Forecasting the foreign exchange market

Título traducido de la contribución: Nuevo Modelo Hibrido de Series de Tiempo Difusas: Pronosticando el mercado de divisas

José Eduardo Medina Reyes, Salvador Cruz Aké, Agustín Ignacio Cabrera Llanos

Escuela Superior de Economía (ESE)

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

Resumen

This work develops a comparison between the volatility prediction of traditional time series models (ARIMA, EGARCH and PARCH), against two new proposed models based on fuzzy theory (FTS-Fuzzy ARIMA Tseng's and FTS-Fuzzy ARIMA Tanaka's). To make this comparison, we estimated the Mexican peso - US dollar exchange rate yield from January 2008 to December 2017. Our main result is that the models based on fuzzy theory generate a better estimate of the volatility. The fuzzy models show a smaller least forecast error than the traditional time series in both; in and out of sample tests; for the volatility in the yield of the Mexican peso - US dollar exchange rate. Therefore, the fuzzy models showed higher efficiency and better reflects the market information.

Título traducido de la contribución	Nuevo Modelo Hibrido de Series de Tiempo Difusas: Pronosticando el mercado de divisas
Idioma original	Inglés
Publicación	Contaduria y Administracion
Volumen	66
N.º	3
DOI	https://doi.org/10.22201/fca.24488410e.2021.2623
Estado	Publicada - 2021

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title = "New Hybrid Fuzzy Time Series Model: Forecasting the foreign exchange market",

abstract = "This work develops a comparison between the volatility prediction of traditional time series models (ARIMA, EGARCH and PARCH), against two new proposed models based on fuzzy theory (FTS-Fuzzy ARIMA Tseng's and FTS-Fuzzy ARIMA Tanaka's). To make this comparison, we estimated the Mexican peso - US dollar exchange rate yield from January 2008 to December 2017. Our main result is that the models based on fuzzy theory generate a better estimate of the volatility. The fuzzy models show a smaller least forecast error than the traditional time series in both; in and out of sample tests; for the volatility in the yield of the Mexican peso - US dollar exchange rate. Therefore, the fuzzy models showed higher efficiency and better reflects the market information.",

keywords = "Fuzzy ARIMA, Fuzzy time series, Fuzzy linear regression, Fuzzy logic",

author = "Reyes, {Jos{\'e} Eduardo Medina} and Ak{\'e}, {Salvador Cruz} and Llanos, {Agust{\'i}n Ignacio Cabrera}",

note = "Publisher Copyright: {\textcopyright} 2019 Universidad Nacional Aut{\'o}noma de M{\'e}xico, Facultad de Contadur{\'i}a y Administraci{\'o}n. This is an open access article under the CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/)",

year = "2021",

doi = "10.22201/fca.24488410e.2021.2623",

language = "Ingl{\'e}s",

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journal = "Contaduria y Administracion",

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TY - JOUR

T1 - New Hybrid Fuzzy Time Series Model

T2 - Forecasting the foreign exchange market

AU - Reyes, José Eduardo Medina

AU - Aké, Salvador Cruz

AU - Llanos, Agustín Ignacio Cabrera

N1 - Publisher Copyright: © 2019 Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración. This is an open access article under the CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/)

PY - 2021

Y1 - 2021

N2 - This work develops a comparison between the volatility prediction of traditional time series models (ARIMA, EGARCH and PARCH), against two new proposed models based on fuzzy theory (FTS-Fuzzy ARIMA Tseng's and FTS-Fuzzy ARIMA Tanaka's). To make this comparison, we estimated the Mexican peso - US dollar exchange rate yield from January 2008 to December 2017. Our main result is that the models based on fuzzy theory generate a better estimate of the volatility. The fuzzy models show a smaller least forecast error than the traditional time series in both; in and out of sample tests; for the volatility in the yield of the Mexican peso - US dollar exchange rate. Therefore, the fuzzy models showed higher efficiency and better reflects the market information.

AB - This work develops a comparison between the volatility prediction of traditional time series models (ARIMA, EGARCH and PARCH), against two new proposed models based on fuzzy theory (FTS-Fuzzy ARIMA Tseng's and FTS-Fuzzy ARIMA Tanaka's). To make this comparison, we estimated the Mexican peso - US dollar exchange rate yield from January 2008 to December 2017. Our main result is that the models based on fuzzy theory generate a better estimate of the volatility. The fuzzy models show a smaller least forecast error than the traditional time series in both; in and out of sample tests; for the volatility in the yield of the Mexican peso - US dollar exchange rate. Therefore, the fuzzy models showed higher efficiency and better reflects the market information.

KW - Fuzzy ARIMA, Fuzzy time series

KW - Fuzzy linear regression

KW - Fuzzy logic

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DO - 10.22201/fca.24488410e.2021.2623

M3 - Artículo

AN - SCOPUS:85099830197

SN - 0186-1042

VL - 66

JO - Contaduria y Administracion

JF - Contaduria y Administracion

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New Hybrid Fuzzy Time Series Model: Forecasting the foreign exchange market

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