Asymmetric Time Series

Wecker, William E.

doi:10.1080/01621459.1981.10477595

Cited by 40 publications

(21 citation statements)

References 4 publications

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“…En la literatura técnica sobre modelos de series de tiempo se observa un uso frecuente de los métodos númericos tradicionales de optimización basados en gradientes (véase [16][17][18]), pero, las funciones de máxima verosilimitud asociadas a algunos modelos NLMA no son diferenciables analíticamente, el cálculo numérico de las derivadas tiene un gran costo computacional y la supeficie de búsqueda es bastante compleja e irregular. Contrario a esto, se observa la inexistencia del uso de las técnicas metaheurísticas como alternativas para la estimación de los parámetros óptimos de dichos modelos.…”

Section: Introductionunclassified

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Cogollo

Velásquez

Jaramillo

2013

rev.ing.univ.Medellin

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RESUMENLa extensión teórica de los modelos lineales de promedios móviles al caso no lineal es directa y sencilla. Sin embargo, el uso práctico de los modelos no lineales de promedios móviles es limitado debido a la complejidad del espacio de parámetros y la imposibilidad de las establecer derivadas analíticas de la función de estimación. En este artículo, se evalúa el uso del algoritmo híbrido Evolución DiferencialOptimización del Enjambre de Partículas para calcular los parámetros óptimos del modelo no lineal de promedios móviles. Los resultados obtenidos muestran que la técnica meta-heurística utilizada es capaz de calcular con mayor precisión los valores de los parámetros del modelo en comparación con los algoritmos tradicionales. Este hallazgo nos alienta a explorar el uso de las meta-heurísticas en la estimación de los parámetros de otros modelos no lineales de promedios móviles.

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Section: Introductionunclassified

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Cogollo

Velásquez

Jaramillo

2013

rev.ing.univ.Medellin

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“…The LjungBox (LB) statistic for ten lags indicates jointly, statistically signi¯cant autocorrelations. Wecker (1981) has shown that the asMA(q) model can generate series with no manifest autocorrelation structure. Hence, it is reasonable to include also higher order lags in the asMA model.…”

Section: Estimation and Evaluationmentioning

confidence: 99%

“…In particular, a di®erent delay in reacting to positive rather than negative innovations (up markets versus down markets) can be represented by an asymmetric parametrization of the conditional mean. The asymmetric moving average (asMA) model proposed by Wecker (1981) is a suitable candidate for this purpose; see, e.g., BrÄ annÄ as and De Gooijer (1994). The model employs two separate (linear)¯lters one for positive and one for negative shocks, i.e.…”

Section: Introductionmentioning

confidence: 99%

Asymmetries in conditional mean and variance: modelling stock returns by asMA‐asQGARCH

Brännäs

Gooijer²

2004

Journal of Forecasting

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)>IJH=?JThe asymmetric moving average model (asMA) is extended to allow for asymmetric quadratic conditional heteroskedasticity (asQGARCH). The asymmetric parametrization of the conditional variance encompasses the quadratic GARCH model of Sentana (1995). We introduce a framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. Some of the new model's moment properties are also derived. Empirical results are given for the daily returns of the composite index of the New York Stock Exchange. There is strong evidence of asymmetry in both the conditional mean and conditional variance functions. In a genuine out-of-sample forecasting experiment the performance of the best¯tted asMA-asQGARCH model is compared to pure asMA and no-change forecasts. This is done both in terms of conditional mean forecasting as well as in terms of risk forecasting.

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“…Some examples of threshold variables z t are: (i) t , for instance when the sign is the shock characteristic that triggers the regime switches (as in Wecker, 1981;Elwood, 1998;Guay and Scaillet, 2003); (ii) ð1 À LÞy t or any other economic variable that is strictly stationary and ergodic (as in De Gooijer, 1998); and (iii) j t j, the shock's size.…”

Section: Tima Shock-size Modelsmentioning

confidence: 99%

“…Threshold moving average (TMA) models have already been considered in Wecker (1981) andin De Gooijer (1998). Both works are centered on presenting the new TMA model and on analyzing some of the moment properties in detail.…”

Section: Introductionmentioning

confidence: 99%

Large shocks vs. small shocks. (Or does size matter? May be so.)

Gonzalo

Martínez

2006

Journal of Econometrics

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What are the shocks that drive economic fluctuations? The answer to this question requires as a first step solving the shock identification issue. This paper proposes a new identification scheme based on two aspects: the long run effect of the shock (permanent or transitory), and the size of the shock (Large or small). This is done by using a threshold integrated moving average model (TIMA) previously introduced in the literature by the authors. Based on this model we develop a testing strategy to determine whether Large and small shocks have different long run effects, as well as whether one of them is purely transitory. The paper analyzes the impulse response function of both types of shocks, and provides the asymptotic results sufficient to implement the above testing strategy. Based on these results we develop a new nonlinear permanent transitory decomposition, that is applied to US stock prices to analyze the quality of the stock market, and to US GNP to investigate the asymmetric behavior of its shocks. r 2005 Elsevier B.V. All rights reserved.JEL classification: C22; C51

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Asymmetric Time Series

Cited by 40 publications

References 4 publications

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Asymmetries in conditional mean and variance: modelling stock returns by asMA‐asQGARCH

Large shocks vs. small shocks. (Or does size matter? May be so.)

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