Robust time series models with trend and seasonal components

Caivano, Michele; Harvey, Andrew; Luati, Alessandra

doi:10.1007/s13209-015-0134-1

Cited by 17 publications

(10 citation statements)

References 14 publications

(10 reference statements)

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“…For Beta-t-EGARCH(1,1), covariance stationarity of y t is supported if λꨓ1 = < 1 (14) For the same model, we also verify the following condition of consistency and asymptotic normality of ML that is demonstrated in the work of Harvey (2013):…”

Section: Statistical Inferencesupporting

confidence: 63%

Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market

Blazsek

Licht²

2018

FSJ

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Recently, the use of dynamic conditional score (DCS) time series models are suggested in the body of literature on time series econometrics. DCS models are robust to extreme observations because those observations are discounted by the score function that updates each dynamic equation. Examples of the DCS models are the quasi-autoregressive (QAR) model and the Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, which measure the dynamics of location and scale, respectively, of the dependent variable. Both QAR and Beta-t-EGARCH discount extreme observations according to a smooth form of trimming. Classical dynamic location and scale models (for example, the AR and the GARCH models) are sensitive to extreme observations. Thus, the AR and the GARCH modelsmay provide imprecise estimates of location and scale dynamics. In the application presented in this paper, we use data from the Shanghai Stock Exchange A-Share Index and the Shenzhen Stock Exchange A-Share Index for the period of 5th January 1998 to 29th December 2017. For the corresponding stock index return time series, a relatively high number of extreme values are observed during the sample period. We find that the statistical performance of QAR plus Beta-t-EGARCH is superior to that of AR plus t-GARCH, due to the robustness of QAR plus Beta-t-EGARCH to extreme unexpected returns.

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Section: Statistical Inferencesupporting

confidence: 63%

Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market

Blazsek

Licht²

2018

FSJ

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“…A partir de los resultados obtenidos, quiere decir que el modelo estimado representa un buen ajuste estadístico de la serie de tiempo del precio de la manzana, dado que se maneja un error relativo de sólo 2% cuando en Wei et al (2010) se acepta un error relativo 5%. Respecto a la identificación del modelo Caivano et al (2016), señala que con series estacionales una doble o triple interacción son suficientes, para ajustarlo.…”

Section: Resultados Y Discusiónunclassified

Análisis del precio de la manzana mediante un modelo SARIMA

Hernandez

Puente

Gómez

2019

Remexca

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El seguimiento y predicción de los precios es una importante herramienta en la toma de decisiones en las actividades productivas. Con base a los datos del precio promedio mensuales de la manzana red delicious a nivel nacional, de enero de 1998 a julio de 2017, publicados por el sistema nacional de información e integración de mercados, con el software Gretlâ. El objetivo de la investigación fue generar información sobre el comportamiento del precio de la manzana mediante un análisis de series de tiempo generándose una predicción para los 12 meses posteriores con un modelo Sarima . Los resultados señalaron que el error relativo para la predicción es 2%, lo cual garantiza pronósticos relativamente buenos, se pronosticó que el precio para julio de 2018 sería $31.99.

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“…Related to the new DCS models with stochastic local level and stochastic seasonality components that are suggested in the present paper, we refer to the works of Harvey (2013) and Harvey and Luati (2014), who introduce the dynamic Student's-t location model that includes stochastic local level and stochastic seasonality components. More recently, Caivano et al (2016) introduce the dynamic EGB2 location model, which includes stochastic local level and stochastic seasonality components. Caivano et al (2016) compare the dynamic Student's-t location and the dynamic EGB2 location models, and demonstrate that extreme observations are discounted in different ways in those models.…”

Section: Review Of the Literature On Dcs Modelsmentioning

confidence: 99%

“…More recently, Caivano et al (2016) introduce the dynamic EGB2 location model, which includes stochastic local level and stochastic seasonality components. Caivano et al (2016) compare the dynamic Student's-t location and the dynamic EGB2 location models, and demonstrate that extreme observations are discounted in different ways in those models. For the Student's-t location model, the score function converges to zero as |v t | → ∞, which is described as a soft form of trimming.…”

Section: Review Of the Literature On Dcs Modelsmentioning

confidence: 99%

“…Secondly, we introduce the new DCS-NIG (Normal-Inverse Gaussian distribution) model (Barndorff-Nielsen and Halgreen 1977) with (1) stochastic local level μ t , (2) stochastic seasonality s t , and (3) DCS-EGARCH scale dynamics for the irregular component v t . The statistical performance of the DCS-NIG model is superior to that of the DCS-EGB2 (Exponential Generalized Beta distribution of the second kind) model (Caivano et al 2016), according to the LL-based parsimony metrics.…”

Section: Introductionmentioning

confidence: 98%

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

Ayala

Blazsek

2018

SERIEs

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In this paper we introduce new Dynamic Conditional Score (DCS) models for the Skew-Gent (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-Gent 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. Keywords Dynamic Conditional Score (DCS) models • Guatemalan Quetzal (GTQ) to United States Dollar (USD) exchange rate • Stochastic seasonality component JEL Classification C22 • C52 • C58 • F31 B Szabolcs Blazsek

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Robust time series models with trend and seasonal components

Cited by 17 publications

References 14 publications

Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market

Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market

Análisis del precio de la manzana mediante un modelo SARIMA

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

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