dor_id: 4119649

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590.#.#.d: Los artículos enviados a la revista "Contaduría y Administración", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); SCOPUS, SCImago Journal Rank (SJR)

561.#.#.u: https://www.fca.unam.mx/

650.#.4.x: Ciencias Sociales y Económicas

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: http://www.cya.unam.mx/index.php/cya/index

351.#.#.b: Contaduría y Administración

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: http://www.cya.unam.mx/index.php/cya/article/view/2623/1582

100.1.#.a: Medina Reyes, José Eduardo; Cruz Aké, Salvador; Cabrera Llanos, Agustín Ignacio

524.#.#.a: Medina Reyes, José Eduardo, et al. (2021). New hybrid fuzzy time series model: Forecasting the foreign exchange market. Contaduría y Administración; Vol. 66, Núm. 3. Recuperado de https://repositorio.unam.mx/contenidos/4119649

720.#.#.a: ConacytConacyt

245.1.0.a: New hybrid fuzzy time series model: Forecasting the foreign exchange market

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Facultad de Contaduría y Administración, UNAM

264.#.0.c: 2021

264.#.1.c: 2020-10-21

653.#.#.a: Economía; finanzas; econometríafuzzy logic; fuzzy arima, fuzzy time series; fuzzy linear regression; economía; finanzas; econometríafuzzy logic; fuzzy arima, fuzzy time series; fuzzy linear regression

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY 4.0 Internacional, https://creativecommons.org/licenses/by/4.0/legalcode.es, fecha de asignación de la licencia 2020-10-21, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico revista_cya@fca.unam.mx

884.#.#.k: http://www.cya.unam.mx/index.php/cya/article/view/2623

001.#.#.#: oai:cya.www.revistas-conacyt.unam.mx:article/2623

041.#.7.h: eng

520.3.#.a: 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 (FTSFuzzy ARIMA Tseng’s and FTS-Fuzzy ARIMA Tanaka’s). To make this comparison, we estimatedthe 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 fuzzymodels showed higher efficiency and better reflects the market information. This paper develops the comparison of the volatility prediction of the traditionalmodels (ARIMA, EGARCH, and PARCH), with respect to the Hybrid Fuzzy TimeSeries and Fuzzy ARIMA Model of Tseng’s and Tanaka’s methodology (FTS-FuzzyARIMA Tseng and FTS-Fuzzy ARIMA Tanaka). For this purpose, it applies to thetime series of the foreign exchange market to forecast the foreign currency exchange rate of Mexican Pesos against American Dollar, the growth rate of the time series data in a daily format from January 2008 to December 2017, to perform the sample test is used January 2018. The main result is that the models based on fuzzy theory generate a better estimate of the volatility of the foreign exchange rate. 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 (FTSFuzzy ARIMA Tseng’s and FTS-Fuzzy ARIMA Tanaka’s). To make this comparison, we estimatedthe 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 fuzzymodels showed higher efficiency and better reflects the market information. This paper develops the comparison of the volatility prediction of the traditionalmodels (ARIMA, EGARCH, and PARCH), with respect to the Hybrid Fuzzy TimeSeries and Fuzzy ARIMA Model of Tseng’s and Tanaka’s methodology (FTS-FuzzyARIMA Tseng and FTS-Fuzzy ARIMA Tanaka). For this purpose, it applies to thetime series of the foreign exchange market to forecast the foreign currency exchange rate of Mexican Pesos against American Dollar, the growth rate of the time series data in a daily format from January 2008 to December 2017, to perform the sample test is used January 2018. The main result is that the models based on fuzzy theory generate a better estimate of the volatility of the foreign exchange rate.

773.1.#.t: Contaduría y Administración; Vol. 66, Núm. 3 (2021)

773.1.#.o: http://www.cya.unam.mx/index.php/cya/index

046.#.#.j: 2021-10-20 00:00:00.000000

022.#.#.a: ISSN electrónico: 2448-8410; ISSN impreso: 0186-1042

310.#.#.a: Trimestral

264.#.1.b: Facultad de Contaduría y Administración, UNAM

758.#.#.1: http://www.cya.unam.mx/index.php/cya/index

doi: https://doi.org/10.22201/fca.24488410e.2021.2623

handle: 6436cf7709b2ee91

harvesting_date: 2021-06-14 11:43:00.0

245.1.0.b: New hybrid fuzzy time series model: forecasting the foreign exchange market|new hybrid fuzzy time series model: forecasting the foreign exchange market|new hybrid fuzzy time series model: forecasting the foreign exchange market

last_modified: 2023-03-22 16:00:00

license_url: https://creativecommons.org/licenses/by/4.0/legalcode.es

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Artículo

New hybrid fuzzy time series model: Forecasting the foreign exchange market

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

Facultad de Contaduría y Administración, UNAM, publicado en Contaduría y Administración, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Facultad de Contaduría y Administración, UNAM
Revista
Contaduría y Administración
Repositorio
Revistas UNAM
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Medina Reyes, José Eduardo, et al. (2021). New hybrid fuzzy time series model: Forecasting the foreign exchange market. Contaduría y Administración; Vol. 66, Núm. 3. Recuperado de https://repositorio.unam.mx/contenidos/4119649

Descripción del recurso

Autor(es)
Medina Reyes, José Eduardo; Cruz Aké, Salvador; Cabrera Llanos, Agustín Ignacio
Colaborador(es)
ConacytConacyt
Tipo
Artículo de Investigación
Área del conocimiento
Ciencias Sociales y Económicas
Título
New hybrid fuzzy time series model: Forecasting the foreign exchange market
Fecha
2020-10-21
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 (FTSFuzzy ARIMA Tseng’s and FTS-Fuzzy ARIMA Tanaka’s). To make this comparison, we estimatedthe 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 fuzzymodels showed higher efficiency and better reflects the market information. This paper develops the comparison of the volatility prediction of the traditionalmodels (ARIMA, EGARCH, and PARCH), with respect to the Hybrid Fuzzy TimeSeries and Fuzzy ARIMA Model of Tseng’s and Tanaka’s methodology (FTS-FuzzyARIMA Tseng and FTS-Fuzzy ARIMA Tanaka). For this purpose, it applies to thetime series of the foreign exchange market to forecast the foreign currency exchange rate of Mexican Pesos against American Dollar, the growth rate of the time series data in a daily format from January 2008 to December 2017, to perform the sample test is used January 2018. The main result is that the models based on fuzzy theory generate a better estimate of the volatility of the foreign exchange rate. 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 (FTSFuzzy ARIMA Tseng’s and FTS-Fuzzy ARIMA Tanaka’s). To make this comparison, we estimatedthe 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 fuzzymodels showed higher efficiency and better reflects the market information. This paper develops the comparison of the volatility prediction of the traditionalmodels (ARIMA, EGARCH, and PARCH), with respect to the Hybrid Fuzzy TimeSeries and Fuzzy ARIMA Model of Tseng’s and Tanaka’s methodology (FTS-FuzzyARIMA Tseng and FTS-Fuzzy ARIMA Tanaka). For this purpose, it applies to thetime series of the foreign exchange market to forecast the foreign currency exchange rate of Mexican Pesos against American Dollar, the growth rate of the time series data in a daily format from January 2008 to December 2017, to perform the sample test is used January 2018. The main result is that the models based on fuzzy theory generate a better estimate of the volatility of the foreign exchange rate.
Tema
Economía; finanzas; econometríafuzzy logic; fuzzy arima, fuzzy time series; fuzzy linear regression; economía; finanzas; econometríafuzzy logic; fuzzy arima, fuzzy time series; fuzzy linear regression
Idioma
eng
ISSN
ISSN electrónico: 2448-8410; ISSN impreso: 0186-1042

Enlaces