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

Translated title of the contribution: New Hybrid Fuzzy Time Series Model: Forecasting the foreign exchange market

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

Escuela Superior de Economía (ESE)

Research output: Contribution to journal › Article › peer-review

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.

Translated title of the contribution	New Hybrid Fuzzy Time Series Model: Forecasting the foreign exchange market
Original language	English
Journal	Contaduria y Administracion
Volume	66
Issue number	3
DOIs	https://doi.org/10.22201/fca.24488410e.2021.2623
State	Published - 2021

Keywords

Fuzzy ARIMA, Fuzzy time series
Fuzzy linear regression
Fuzzy logic

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10.22201/fca.24488410e.2021.2623

Cite this

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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.",

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

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year = "2021",

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

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volume = "66",

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

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SN - 0186-1042

VL - 66

JO - Contaduria y Administracion

JF - Contaduria y Administracion

IS - 3

ER -

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

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Keywords

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