Conditional Heteroskedasticity Research Papers - Academia.edu (original) (raw)

In addition to clear-cut seasonality in mean and variance, weekly Dutch temperature data appear to have a strong asymmetry in the impact of unexpectedly high or low temperatures on conditional volatility. Furthermore, this asymmetry also shows fairly pronounced seasonal variation. To describe these features, we propose a univariate seasonal time series model with asymmetric conditionally heteroskedastic errors. We fit this (and other, nested) model(s) to 25 years of weekly data. We evaluate its forecasting performance for 5 years of hold-out data and find that the imposed asymmetry leads to better out-of-sample forecasts of temperature volatility.

- by
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- Time Series, Multidisciplinary, Seasonality, Seasonal variation

Александр Цыплаков † Новосибирский государственный университет, Новосибирск, Россия В настоящем эссе обсуждаются базовые понятия прогнозирования временных рядов и излагаются традиционные подходы к прогнозированию в классических моделях... more

In this paper we propose and implement a methodology for testing and estimating GARCH effects in a panel data context. We propose simple tests based on OLS and LSDV residuals to determine whether GARCH effects exist and to test for individual effects in the conditional variance. Estimation of the model is based on direct maximization of the log-likelihood function by

We propose a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments. Jump arrival is governed by a heterogeneous Poisson process. The intensity is directed by a latent stochastic autoregressive process, while the jump-size distribution allows for conditional heteroskedasticity. Model evaluation focuses on the dynamics of the conditional distribution of returns using

- by Thomas McCurdy
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- MCMC, Higher Order Thinking, Model Evaluation, Size Distribution

The performances of alternative two-stage estimators for the endogenous switching regression model with discrete dependent variables are compared, with regard to their usefulness as starting values for maximum likelihood estimation. This is especially important in the presence of large correlation coe½cients, in which case maximum likelihood procedures have di½culties to converge. Monte-Carlo simulations indicate that an estimator that corrects for conditional heteroskedasticity of the residuals is superior in almost all instances, and especially when maximum likelihood is problematic. This result is also obtained in an empirical example in which o¨-farm work participation equations of farm women are conditional on farm work participation status.

- by Ayal Kimhi
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- Economics, Monte Carlo Simulation, Empirical Economics, Maximum Likelihood

We derive indirect estimators of multivariate conditionally heteroskedastic factor models in which the volatilities of the latent factors depend on their past values. SpeciÞcally, we calibrate the analytical score of a Kalman-Þlter approximation, taking into account the inequality constraints on the auxiliary model parameters. We also study the determinants of the biases in the parameters of this approximation, and its quality. Moreover, we propose sequential indirect estimators that can handle models with large cross-sectional dimensions. Finally, we analyse the small sample behaviour of our indirect estimators and the approximate maximum likelihood procedures through an extensive Monte Carlo experiment. helpful comments and suggestions. Of course, the usual caveat applies. Financial support from MIUR through the project "SpeciÞcation, estimation and testing of latent variable models. Applications to the analysis and forecasting of economic and Þnancial time series" is gratefully acknowledged. Thanks are also due to Javier Mencía for his help in producing .

- by Enrique Sentana and +1
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- Volatility, Kalman Filter, Arch, Maximum Likelihood

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for largescale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small sample behaviour of our indirect estimators and several likelihood-based procedures through an extensive Monte Carlo experiment with empirically realistic designs. Finally, we apply our procedures to weekly returns on the Dow 30 stocks. Economics (Rimini, 2007) for helpful comments and suggestions. Of course, the usual caveat applies. Financial support from MIUR through the project "Specification, estimation and testing of latent variable models. Applications to the analysis and forecasting of economic and financial time series" is gratefully acknowledged. Thanks are also due to Javier Mencía for his help in producing and the asymptotic information matrices, and to Gian

- by Enrique Sentana and +1
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- Approximation Theory, Econometrics, Data Analysis, Numerical Analysis

This paper develops a framework for the construction and analysis of misspecification tests for GARCH models and proposes new tests for asymmetry and non-linearity. It is argued that the asymmetry test of Engle and Ng (1993) and the non-linearity test of Lundbergh and Teräsvirta (2002) are both neither asymptotically valid (due to neglected non-negligible estimation effects) nor locally optimal (since

- by Andreea Halunga
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- Monte Carlo, Conditional Heteroskedasticity, GARCH Model

This study explores the direction and nature of causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). These are the most liquid and widely traded currency pairs in the world and make up about 90% of total

- by Stelios Bekiros
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- Economics, Time Series, Higher Order Thinking, United States
- by Timo Teräsvirta
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- Econometrics, Nonlinear Time Series, Standard Error, Lagrange Multiplier

JEL classification: C14 C18 G13

- by Ser-Huang Poon
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- Applied Mathematics, Financial Crisis, Financial time series, Extreme Value Theory

This article proposes a multivariate model of inflation with conditionally heteroskedastic common and country-specific components. The model is estimated in one-step via Quasi-Maximum Likelihood for the G7 countries for the period Q1-1960 to Q4-2009. It is found that various model specifications considered fit well the first and second order dynamics of inflation in the G7. The estimated volatility of the common inflation component captures the international effects of the 'Great Moderation' and of the 'Great Recession'. The model also shows promising capabilities for forecasting inflation in several countries.

The present study investigates the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover two periods with the latter being significantly more turbulent. Apart from the conventional linear Granger test we apply a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration. In addition to the traditional pairwise analysis, we test for causality while correcting for the effects of the other variables. To check if any of the observed causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VECM filtered residuals. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Whilst the linear causal relationships disappear after VECM cointegration filtering, nonlinear causal linkages in some cases persist even after GARCH filtering in both periods. This indicates that spot and futures returns may exhibit asymmetries and statistically significant higherorder moments. Moreover, the results imply that if nonlinear effects are accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time.

- by Stelios Bekiros
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- Mechanical Engineering, Energy Economics, Data Analysis, Causality

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQindex data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect.

- by Markus Haas
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- Econometrics, Financial Econometrics, Financial, Indexation

To match the stylized facts of high frequency financial time series precisely and parsimoniously, this paper presents a finite mixture of conditional exponential power distributions where each component exhibits asymmetric conditional heteroskedasticity. We provide weak stationarity conditions and unconditional moments to the fourth order. We apply this new class to Dow Jones index returns. We find that a two-component mixed exponential power distribution dominates mixed normal distributions with more components, and more parameters, both in-sample and out-of-sample. In contrast to mixed normal distributions, all the conditional variance processes become stationary. This happens because the mixed exponential power distribution allows for component-specific shape parameters so that it can better capture the tail behaviour. Therefore, the more general new class has attractive features over mixed normal distributions in our application: less components are necessary and the conditional variances in the components are stationary processes. Results on NASDAQ index returns are similar.

- by Mohammed Bouaddi
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- Applied Mathematics, Econometrics, High Frequency, Financial time series

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH. .es (A. García-Ferrer), ester.gonzalez@uc3m.es (E. González-Prieto), daniel.pena@uc3m.es (D. Peña). multivariate modelling approach is required. Multivariate GARCH (MGARCH) models should be able to explain the structure of the covariance matrix of large financial datasets, and also represent the dynamics of their conditional variances and covariances. Depending on the parametrization of the conditional covariance matrix, different specifications for MGARCH models have been proposed in the literature (see for example the survey by . Two popular MGARCH specifications are the VEC model , which is an extension of the univariate GARCH model (see Engle, Granger, & Kraft, 1984, for an ARCH version), and the BEKK model , which can be seen as a restricted version of the VEC model. However, the number of parameters requiring es-0169-2070/$ -see front matter

- by Daniel Peña
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- Marketing, Econometrics, Independent Component Analysis, Forecasting

In this paper we propose a general series method to estimate a semiparametric partially linear varying coefficient model. We establish the consistency and \sqrtn-normality property of the estimator of the finite-dimensional parameters of the model. We further show that, when the error is conditionally homoskedastic, this estimator is semiparametrically efficient in the sense that the inverse of the asymptotic variance of the estimator of the finite-dimensional parameter reaches the semiparametric efficiency bound of this model. A small-scale simulation is reported to examine the finite sample performance of the proposed estimator, and an empirical application is presented to illustrate the usefulness of the proposed method in practice. We also discuss how to obtain an efficient estimation result when the error is conditional heteroskedastic.

- by Ibrahim S Ahmad
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- Econometrics, Statistics, Conditional Heteroskedasticity

The attractive possibility that financial indices may be chaotic has been the subject of much study. In this paper we address two specific questions: "Masked by stochasticity, do financial data exhibit deterministic nonlinearity?", and "If so, so what?". We examine daily returns from three financial indicators: the Dow Jones Industrial Average, the London gold fixings, and the USD-JPY exchange rate. For each data set we apply surrogate data methods and nonlinearity tests to quantify determinism over a wide range of time scales (from 100 to 20,000 days). We find that all three time series are distinct from linear noise or conditional heteroskedastic models and that there therefore exists detectable deterministic nonlinearity that can potentially be exploited for prediction.

- by Michael Small
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- Applied Mathematics, Econometrics, Time Series, Nonlinear dynamics

An integration test against fractional alternatives is suggested for univariate time series. The new test is a completely regression based, lag augmented version of the LM test by Robinson (1991, Journal of Econometrics 47, 67-84). Our main contributions, however, are the following. First, we let the short memory component follow a general linear process. Second, the innovations driving this process are martingale differences with eventual conditional heteroskedasticity that is accounted for by means of White's standard errors. Third, we assume the number of lags to grow with the sample size, thus approximating the general linear process. Under these assumptions limiting normality of the test statistic is retained. The usefulness of the asymptotic results for finite samples is established in Monte Carlo experiments. In particular, we study several strategies of model selection.

- by Matei Demetrescu
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- Econometrics, Econometric Theory, Time Series, Long Memory

R (1985); Hall et al. (1988)l has found that the distribution of futures prices is not normal but leptokurtic. Specifically, the empirical distributions of daily price changes have more observations around the means and in the extreme tails than does a normal distribution. Leptokurtosis also appears in stock returns l and exchange rate changes l. Further, nonlinear dependence has been found in futures price changes ; l. Yet, empirical research on market anomalies has either ignored the non-normality and dependence or resorted to nonparametric tests which generally are less powerful than parametric tests.

- by Seung-Ryong Yang
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- Nonlinear dynamics, Futures markets, Conditional Heteroskedasticity

It is well-known that financial data sets exhibit conditional heteroskedasticity. GARCH type models are often used to model this phenomenon. Since the distribution of the rescaled innovations is generally far from a normal distribution, a semiparametric approach is advisable. Several publications observed that adaptive estimation of the Euclidean parameters is not possible in the usual parametrization when the distribution of the rescaled innovations is the unknown nuisance parameter. However, there exists a reparametrization such that the efficient score functions in the parametric model of the autoregression parameters are orthogonal to the tangent space generated by the nuisance parameter, thus suggesting that adaptive estimation of the autoregression parameters is possible. Indeed, we construct adaptive and hence efficient estimators in a general GARCH in mean type context including integrated GARCH models.

- by Chris Klaassen and +1
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- Econometrics, Economic Theory, ARCH model, Conditional Heteroskedasticity

We show that a class of microeconomic behavioral models with interacting agents, derived from and , can replicate the empirical long-memory properties of the two first-conditional moments of financial time series. The essence of these models is that the forecasts and thus the desired trades of the individuals in the markets are influenced, directly or indirectly, by those of the other participants. These "field effects" generate "herding" behavior that affects the structure of the asset price dynamics. The series of returns generated by these models display the same empirical properties as financial returns: returns are I (0), the series of absolute and squared returns display strong dependence, and the series of absolute returns do not display a trend. Furthermore, this class of models is able to replicate the common long-memory properties in the volatility and covolatility of financial time series revealed by . These properties are investigated by using various model-independent tests and estimators, that is, semiparametric and nonparametric, introduced by Lo (

- by Alan Kirman
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- Long Memory, Financial time series, Behavior Modeling, Herd Behavior

In this article we propose a statistical model to adjust, interpolate and forecast the term structure of interest rates. This model is based on extensions for the term structure model of interest rates proposed by [Diebold & Li, 2006], through a Bayesian estimation using Markov Chain Monte Carlo. The proposed extensions involve the use of a more flexible parametric form for the yield curve, making all parameters time-varying using a structure of latent factors, and adding a stochastic volatility structure to control the presence of conditional heteroscedasticity observed in the interest rates. The Bayesian estimation enables the exact distribution of estimators in finite samples, and as a sub product, the estimation enables obtaining the distribution of forecasts for the term structure of interest rates. The methodology developed does not need a pre-interpolation of the yield curve as it happens in some econometric models of term structure. We do an empirical exercise of this methodology in which we adjust daily data of the term structure of interest rates implicit in Swap DI-PRÉ contracts traded in the Mercantile and Futures Exchange (BM&F)

- by Luiz Hotta
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- Law, Economics, Time Use, Financial Analysis

- by Enrique Sentana and +1
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- Approximation Theory, Econometrics, Data Analysis, Numerical Analysis

proposed a test for the detection of changes of the unconditional variance which has been used in financial time series analysis. In this article we show some serious drawbacks for using this test with this type of data. Specifically, it suffers important size distortions for leptokurtic and platykurtic innovations. Moreover, the size distortions are more extreme for heteroskedastic conditional variance processes. These results invalidate in practice the use of the test for financial time series.

- by Andreu Sansó
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- Financial time series, Conditional Heteroskedasticity

Censored Least Absolute Deviations (CLAD) estimator for the censored linear regression model has been regarded as a desirable alternative to maximum likelihood estimation methods due to its robustness to conditional heteroskedasticity and distributional misspeci cation of the error term. However, the CLAD estimation procedure has failed in certain empirical applications due to the restrictive nature of the \full rank" condition it requires. This condition can be especially problematic when the data is heavily censored. In this paper we introduce estimation procedures for heteroskedastic censored linear regression models with a much weaker identi cation restriction than that required for the CLAD, and which are exible enough to allow for various degrees of censoring. The new estimators are shown to have desirable asymptotic properties and perform well in small scale simulation studies, and can thus be considered as viable alternatives for estimating censored regression models, especially for applications in which the CLAD fails. JEL Classi cation: C14,C23,C24 . We are grateful to B.E. Honor e, J.L. Powell and two anonymous referees for their helpful comments.

- by shakeeb khan
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- Econometrics, Linear Model, Maximum Likelihood, Heteroscedasticity

In this paper the class of Bilinear GARCH (BL-GARCH) models is proposed. BL-GARCH models allow to capture asymmetries in the conditional variance of financial and economic time series by means of interactions between past shocks and volatilities. The availability of likelihood based inference is an attractive feature of BL-GARCH models. Under the assumption of conditional normality, the log-likelihood function can be maximized by means of an EM type algorithm. The main reason for using the EM algorithm is that it allows to obtain parameter estimates which naturally guarantee the positive definiteness of the conditional variance with no need for additional parameter constraints. We also derive a robust LM test statistic which can be used for model identification. Finally, the effectiveness of BL-GARCH models in capturing asymmetric volatility patterns in financial time series is assessed by means of an application to a time series of daily returns on the NASDAQ Composite stock market index.

- by Giuseppe Storti
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- Statistics, Time Series, Stock Market, Parameter estimation

Nos planteamos analizar el comportamiento dinámico lineal y no lineal de los rendimientos intradía del índice bursátil Eurostoxx50 y de su contrato de futuro, los cuales debido a su relativa juventud, no han sido previamente analizados. Realizamos el estudio tanto desde la perspectiva individual como conjunta. Los resultados del contraste BDS indican que las variables no son iid y que la dinámica individual no lineal detectada no puede explicarse únicamente por la presencia de heteroscedasticidad condicional. Para el estudio de las relaciones dinámicas entre los precios de ambos mercados permitimos que el proceso de ajuste ante desequilibrios de la relación de cointegración a largo plazo sea no lineal. Constatamos que el Eurostoxx50 y su contrato de futuro están cointegrados y que el proceso de ajuste no es lineal. Finalmente, encontramos que los flujos de información entre mercados son bidireccionales tanto en el ámbito lineal como en el no lineal.

The present study investigates the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover two periods October 1991-October 1999 and November 1999-October 2007, with the latter being significantly more turbulent. Apart from the conventional linear Granger test we apply a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration. In addition to the traditional pairwise analysis, we test for causality while correcting for the effects of the other variables. To check if any of the observed causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VECM filtered residuals. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Whilst the linear causal relationships disappear after VECM cointegration filtering, nonlinear causal linkages in some cases persist even after GARCH filtering in both periods. This indicates that spot and futures returns may exhibit asymmetries and statistically significant higherorder moments. Moreover, the results imply that if nonlinear effects are accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time.

- by Stelios Bekiros
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- Mechanical Engineering, Economics, Energy Economics, Data Analysis

We analyze the implications for portfolio management of accounting for conditional heteroskedasticity and sudden changes in volatility, based on a sample of weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005. To that end, we first proceed to utilize the ICSS algorithm to detect long-term volatility shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor's time horizon. We repeat the same procedure for artificial data generated from semi-parametric estimates of the distribution functions of returns, which account for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead to an overestimation of financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock and bond indices contribute to portfolio diversification. r .cl (V. Fernandez), blucey@tcd.ie (B.M. Lucey).

- by Viviana Fernandez
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- Mathematical Physics, Quantum Physics, Portfolio Management, Wavelets

In this paper we extend previous research into the non-linear dynamics of stock returns. have examined whether stock returns exhibit significant non-linear dependence and, in particular, behaviour consistent with chaotic dynamics. Based on the accumulated evidence to date, stock returns display clear evidence of nonlinear behaviour, but the evidence for chaotic dynamics is, at best, weak. However, with the exception of Scheinkman and who examine a small number of individual stock returns series, most prior work has focused analysis on stock indices rather than individual stock returns. It is possible that stock index returns behave differently to the underlying stocks and that the aggregation process used to compute index returns masks more complex, and possibly chaotic, dynamics of individual stock returns series. Further, most prior work examines US stock returns data. As yet it is unclear whether stock prices generated in different economic and institutional

- by pradeep yadav
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- Marketing, Business Ethics, Corporate Social Responsibility, Social Marketing

Asset returns exhibit clustering of volatility throughout the year. This paper proposes a class of models featuring periodicity in conditional heteroskedasticity. The periodic structures in GARCH models share many properties with periodic ARMA processes studied by Gladyshev (1961), Tiao and Grupe (1980) and others. We describe the relation between periodic GARCH processes and time-invariant (seasonal) GARCH processes. Besides the periodic

- by Tim Bollerslev
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- Monte Carlo, Seasonality, Exchange rate, Conditional Heteroskedasticity
- by Bruce Mizrach and +1
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- Time Series, GARCH, Exchange rate, Financial Market

This paper analyzes the relationship between stock returns and exchange rate changes in international markets and examines how well exchange rate volatility explains movements in stock market returns. The model-based predictions are evaluated on several cost functions. Results from such analysis can be used to appraise the need for hedging. Of the three examined stock indexes, the FTSE was found

- by Adusei Jumah and +1
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- International Marketing, Exchange rate, Exchange Rate Exposure, Inflation and Stock Market Returns

- by viviana fernandez
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- Mathematical Physics, Quantum Physics, Portfolio Management, Wavelets

We examine the relation between US stock market returns and the US business cycle for the period 1960 - 2003 using a new methodology that allows us to estimate a time-varying equity premium. We identify two channels in the transmission mechanism. One is through the mean of stock returns via the equity risk premium, and the other is through the volatility of returns. We provide support for previous findings based on simple correlation analysis that the relation is asymmetric with downturns in the business cycle having a greater negative impact on stock returns than the positive effect of upturns. We also obtain a new result, that demand and supply shocks affect stock returns differently. We find that negative supply shocks are a very important source of increases in the risk premium. Our model of the relation between returns and their volatility encompasses the CAPM and the results demonstrate the importance of allowing for a time-varying price of volatility risk. The model is implem...

- by M. Wickens
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- Equity Premium, Asymmetry, Business Cycle, Demand and Supply
- by Mustafa Pınar
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- Applied Mathematics, Time Series, Nonlinear Programming, Optimization Problem

We investigate alternative unconditional and conditional distributional models for the returns on Japan's Nikkei 225 stock market index. Among them is the recently introduced class of ARMA-GARCH models driven by α-stable (or stable... more

- by Zari Rachev
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- Persistence, Risk assessment, Stock Market, Volatility

We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one.

- by Carlos Velasco
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- Econometrics, Higher Order Thinking, Specification Tests, Sample Size
- by Chris Choque
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- Economics, Value at Risk, Exchange rate, International Reserves

- by Luisa Nieto
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- Nonlinear dynamics, Information Flow, Dynamic behaviour of materials, Indexation

Generally, how to satisfy the deadline constraint is the major issue in solving real-time scheduling. Recently, neural network using competitive learning rule provides a highly effective method and deriving a sound solution for scheduling problem with less network complexity. However, due to the availability of resources, the machines may not reach full utilization. To facilitate the problem the extra neuron is introduced to the competitive neural network (CHNN). This study tries to impose slack neuron on CHNN with respect to process time and deadline constraints. Simulation results reveal that the competitive neural network imposed on the proposed energy function with slack neurons integrated ensures an appropriate approach of solving this class of scheduling problems of single or multiple identical machines.

Some statistical properties of a vector autoregressive process with Markov-switching coefficients are considered+ Sufficient conditions for this nonlinear process to be covariance stationary are given+ The second moments of the process are derived under the conditions+ The autocovariance matrix decays at exponential rate, permitting the application of the law of large numbers+ Under the stationarity conditions, although sharing the "mean-reverting" property with conventional linear stationary processes, the process offers richer short-run dynamics such as conditional heteroskedasticity, asymmetric responses, and occasional nonstationary behavior+ I thank the co-editor Bruce Hansen and two referees for their constructive comments+ I am also grateful to Ron Bewley and Graham Elliott for their helpful suggestions+

- by Minxian Yang
- •
- Econometrics, Econometric Theory, Law of large numbers, Markov switching

Standard real business cycle models must rely on total factor productivity (TFP) shocks to explain the observed comovement of consumption, investment, and hours worked. This paper shows that a neoclassical model consistent with observed heterogeneity in labor supply and consumption can generate comovement in the absence of TFP shocks. Intertemporal substitution of goods and leisure induces comovement over the business cycle through heterogeneity in the consumption behavior of employed and unemployed workers. This result owes to two model features introduced to capture important characteristics of U.S. labor market data. First, individual consumption is affected by the number of hours worked: Employed agents consume more on average than the unemployed do. Second, changes in the employment rate, a central factor explaining variation in total hours, affect aggregate consumption. Demand shocks--such as shifts in the marginal efficiency of investment, as well as government spending shock...

- by Antonio García-ferrer
- •
- Marketing, Econometrics, Independent Component Analysis, Forecasting

- by Markus Haas
- •
- Econometrics, Financial Econometrics, Financial, Indexation

In this paper we investigate the effects of volatility of the fundamental determinants of trade on trade flows in México during the period 1991-2008. Our import and export functions are based on the well known imperfect substitute goods model of trade. We focus on the effects of real exchange rate as well as measures of relative prices and real income

- by Rodolfo Cermeño and +1
- •
- Price Elasticity, Empirical Study, Exchange rate, Vector Error Correction

This paper uses the approach suggested by , and to examine the forecasting accuracy of stock price index models for industrialised markets. The focus of this paper is to compare the Mean Absolute Percentage Error (MAPE) of three models, that is, the Random Walk model, the Single Exponential Smoothing model and the Conditional Heteroskedastic model with the MAPE of the benchmark Naïve Forecast 1 case. We do not evidence that a single model to provide better forecasting accuracy results compared to other models.

- by Apostolos Dasilas
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- Applied Economics, Exponential Smoothing, Random Walk, Stock Price