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GARCH
Terms modified by GARCH Selected AbstractsMarketing Category Forecasting: An Alternative of BVAR-Artificial Neural Networks¶DECISION SCIENCES, Issue 4 2000James J. Jiang ABSTRACT Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models. [source] Modelling volatility clustering in electricity price return series for forecasting value at riskEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 1 2009R. G. Karandikar Abstract Modelling of non-stationary time series using regression methodology is challenging. The wavelet transforms can be used to model non-stationary time series having volatility clustering. The traditional risk measure is variance and now a days Value at Risk (VaR) is widely used in finance. In competitive environment, the prices are volatile and price risk forecasting is necessary for the market participants. The forecasting period may be 1 week or higher depending upon the requirement. In this paper, a model is developed for volatility clustering in electricity price return series and its application for forecasting VaR is demonstrated. The first model is using GARCH (1, 1). The VaR of variance rate series, that is worst-case volatility is calculated using variance method using wavelet transform. The model is used to forecast variance rate (volatility) for a sample case of 1-week half-hourly price return series. The second model developed is for forecasting VaR for price return series of 440 days. This model is developed using wavelets via multi-resolution analysis and uses regime-switching technique. The historical data of daily average prices is obtained from 100% pool type New South Wales (NSW), a zonal market of National Electricity Market (NEM), Australia. Copyright © 2007 John Wiley & Sons, Ltd. [source] Multivariate GARCH Modeling of Exchange Rate Volatility Transmission in the European Monetary SystemFINANCIAL REVIEW, Issue 1 2000Colm Kearney C32/F31/G15 Abstract We construct a series of 3-, 4- and 5-variable multivariate GARCH models of exchange rate volatility transmission across the important European Monetary System (EMS) currencies including the French franc, the German mark, the Italian lira, and the European Currency Unit. The models are estimated without imposing the common restriction of constant correlation on both daily and weekly data from April 1979,March 1997. Our results indicate the importance of checking for specification robustness in multivariate Generalized Autoregressive Conditional Heleroskedasticity (GARCH) modeling, we find that increased temporal aggregation reduces observed volatility transmission, and that the mark plays a dominant position in terms of volatility transmission. [source] Gradualism, Transparency and the Improved Operational Framework: A Look at Overnight Volatility Transmission,INTERNATIONAL FINANCE, Issue 2 2009Silvio Colarossi This paper proposes a possible way of assessing the effect on interest rate dynamics of changes in the decision-making method, in the communication strategy and in the operational framework of a central bank. Through a generalized autoregressive conditional heteroscedasticity (GARCH) specification, we show that the United States and the euro area displayed a limited but significant spillover of volatility from money market to longer-term rates. We then checked the stability of this phenomenon in the most recent period of improved policy-making and found empirical evidence to show that the transmission of overnight volatility along the yield curve had entirely disappeared. [source] Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH modelINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 4 2010Anastassios A. Drakos Abstract In this paper we model the return volatility of stocks traded in the Athens Stock Exchange using alternative GARCH models. We employ daily data for the period January 1998 to November 2008 allowing us to capture possible positive and negative effects that may be due to either contagion or idiosyncratic sources. The econometric analysis is based on the estimation of a class of five GARCH models under alternative assumptions with respect to the error distribution. The main findings of our analysis are: first, based on a battery of diagnostic tests it is shown that the normal mixture asymmetric GARCH (NM-AGARCH) models perform better in modeling the volatility of stock returns. Second, it is shown that with the use of the Kupiec's tests for in-sample and out-of-sample forecasting performance the evidence is mixed as the choice of the appropriate volatility model depends on the trading position under consideration. Third, at the 99% confidence interval the NM-AGARCH model with skewed Student-distribution outperforms all other competing models both for in-sample and out-of-sample forecasting performance. This increase in predictive performance for higher confidence intervals of the NM-AGARCH model with skewed Student-distribution makes this specification consistent with the requirements of the Basel II agreement. Copyright © 2010 John Wiley & Sons, Ltd. [source] Black and official exchange rate volatility and foreign exchange controls: evidence from GreeceINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2001Angelos Kanas F31; F32; C22; C52 Abstract This paper examines the issue of volatility and capital controls to the official and black market exchange rates of the Greek Drachma using the monthly exchange rate against the US dollar for the period 1975,1993. Specifically, we apply a GARCH(1,,1) model to study the behaviour of the official and black market drachma/dollar exhange rate. The main findings of the analysis are: (i) in contrast to the findings of previous studies using monthly rates, GARCH processes characterize the drachma/dollar exchange rate series in both markets; (ii) the relaxation of foreign exchange controls increased the volatility of the exchange rate in the official market as implied by theory; (iii) the persistence of volatility is reduced when account is taken of the liberalization process of capital movements; and (iv) The forecasts of volatility are improved when the GARCH forecasts are used against traditional measures. Copyright © 2001 John Wiley & Sons, Ltd. [source] Optimal Hedging Ratios for Wheat and Barley at the LIFFE: A GARCH ApproachJOURNAL OF AGRICULTURAL ECONOMICS, Issue 2 2000P. J. Dawson Over 100,000 futures contracts for cereals are traded annually on the London International Financial Futures Exchange. The proportion of the spot position held as futures contracts - the hedging ratio - is critical to traders and traditional estimates, using OLS, are constant over time. In this paper, we estimate time-varying hedging ratios for wheat and barley contracts using a multivariate generalised autoregressive conditional heteroscedasticity (GARCH) model. Results indicate that GARCH hedging ratios do change through time. Moreover, risk using the GARCH hedge is reduced significantly by around 4 per cent for wheat and 2 per cent for barley relative to the no hedge position, and significantly by around 0.2 per cent relative to the constant hedge. The optimal, expected utility-maximising, and the risk-minimising hedging ratios are equivalent. [source] Asymmetric power distribution: Theory and applications to risk measurementJOURNAL OF APPLIED ECONOMETRICS, Issue 5 2007Ivana Komunjer Theoretical literature in finance has shown that the risk of financial time series can be well quantified by their expected shortfall, also known as the tail value-at-risk. In this paper, I construct a parametric estimator for the expected shortfall based on a flexible family of densities, called the asymmetric power distribution (APD). The APD family extends the generalized power distribution to cases where the data exhibits asymmetry. The first contribution of the paper is to provide a detailed description of the properties of an APD random variable, such as its quantiles and expected shortfall. The second contribution of the paper is to derive the asymptotic distribution of the APD maximum likelihood estimator (MLE) and construct a consistent estimator for its asymptotic covariance matrix. The latter is based on the APD score whose analytic expression is also provided. A small Monte Carlo experiment examines the small sample properties of the MLE and the empirical coverage of its confidence intervals. An empirical application to four daily financial market series reveals that returns tend to be asymmetric, with innovations which cannot be modeled by either Laplace (double-exponential) or Gaussian distribution, even if we allow the latter to be asymmetric. In an out-of-sample exercise, I compare the performances of the expected shortfall forecasts based on the APD-GARCH, Skew- t -GARCH and GPD-EGARCH models. While the GPD-EGARCH 1% expected shortfall forecasts seem to outperform the competitors, all three models perform equally well at forecasting the 5% and 10% expected shortfall. Copyright © 2007 John Wiley & Sons, Ltd. [source] Normal mixture GARCH(1,1): applications to exchange rate modellingJOURNAL OF APPLIED ECONOMETRICS, Issue 3 2006Carol Alexander Some recent specifications for GARCH error processes explicitly assume a conditional variance that is generated by a mixture of normal components, albeit with some parameter restrictions. This paper analyses the general normal mixture GARCH(1,1) model which can capture time variation in both conditional skewness and kurtosis. A main focus of the paper is to provide evidence that, for modelling exchange rates, generalized two-component normal mixture GARCH(1,1) models perform better than those with three or more components, and better than symmetric and skewed Student's t -GARCH models. In addition to the extensive empirical results based on simulation and on historical data on three US dollar foreign exchange rates (British pound, euro and Japanese yen), we derive: expressions for the conditional and unconditional moments of all models; parameter conditions to ensure that the second and fourth conditional and unconditional moments are positive and finite; and analytic derivatives for the maximum likelihood estimation of the model parameters and standard errors of the estimates. Copyright © 2006 John Wiley & Sons, Ltd. [source] How Persistent is Stock Return Volatility?JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 5-6 2007An Answer with Markov Regime Switching Stochastic Volatility Models Abstract:, We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed. [source] Software Review: Theory and Practice of Econometric Modelling using PcGive10JOURNAL OF ECONOMIC SURVEYS, Issue 4 2001Giovanni Urga This review offers a guided tour to PcGive 10 modules for econometrics analysis of time series (PcGive), limited dependent variable (LogitJD) and static and dynamic panel data analyses (DPD), financial econometric (GARCH) and time series (ARFIMA) modelling. Several empirical applications are reported to illustrate the package. [source] Supply response and price volatility in the Greek broiler marketAGRIBUSINESS : AN INTERNATIONAL JOURNAL, Issue 1 2010Anthony N. Rezitis The authors examine the supply response of the Greek broiler market. A generalized autoregressive conditional heteroskedasticity (GARCH) process is used to estimate expected price and price volatility; price and supply equations are estimated jointly. In addition to the standard GARCH model, several different symmetric, asymmetric, and nonlinear GARCH models are estimated. These models use different conditional variance specifications (linear or nonlinear) to grasp some additional empirical regularity of data like asymmetry. Asymmetric price volatility means that different volatility is recorded in the case of a fall in prices than an increase in prices by the same amount. The possible existence of asymmetry in the producer's price volatility gives useful information about market structure and possible market power. The empirical results indicate that among the estimated GARCH models the nonlinear asymmetric GARCH model (NAGARCH) seems to better describe producers' price volatility of the Greek broiler industry. Furthermore, the empirical findings show that price volatility is an important risk factor and broiler feed price is the most significant cost factor of the supply response function. Finally, the model provides forecasts for quantity supplied, producers' price, and price volatility. [EconLit. Classifications: Q110, C510, D200]. © 2010 Wiley Periodicals, Inc. [source] Forecasting volatility with support vector machine-based GARCH modelJOURNAL OF FORECASTING, Issue 4 2010Shiyi Chen Abstract Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN-GARCH models by using two evaluation measures and robust Diebold,Mariano tests. The real data used in this study are daily GBP exchange rates and NYSE composite index. Empirical results from both simulation and real data reveal that, under a recursive forecasting scheme, SVM-GARCH models significantly outperform the competing models in most situations of one-period-ahead volatility forecasting, which confirms the theoretical advantage of SVM. The standard GARCH model also performs well in the case of normality and large sample size, while EGARCH model is good at forecasting volatility under the high skewed distribution. The sensitivity analysis to choose SVM parameters and cross-validation to determine the stopping point of the recurrent SVM procedure are also examined in this study. Copyright © 2009 John Wiley & Sons, Ltd. [source] Optimal sampling frequency for volatility forecast models for the Indian stock marketsJOURNAL OF FORECASTING, Issue 1 2009Malay Bhattacharyya Abstract This paper evaluates the performance of conditional variance models using high-frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non-normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd. [source] Forecasting volatility by means of threshold modelsJOURNAL OF FORECASTING, Issue 5 2007M. Pilar Muñoz Abstract The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out-of-sample forecasting ability of the SETAR-Threshold GARCH (SETAR-TGARCH) and the SETAR-Threshold Stochastic Volatility (SETAR-THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ,true' underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR-THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd. [source] Bias in the estimation of non-linear transformations of the integrated variance of returnsJOURNAL OF FORECASTING, Issue 7 2006Richard D. F. Harris Abstract Volatility models such as GARCH, although misspecified with respect to the data-generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non-linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non-linear transformations of these forecasts will be biased estimators of the same non-linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non-linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non-linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in-sample and out-of-sample forecasts of three non-linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] A Bayesian threshold nonlinearity test for financial time seriesJOURNAL OF FORECASTING, Issue 1 2005Mike K. P. So Abstract We propose in this paper a threshold nonlinearity test for financial time series. Our approach adopts reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities of two competitive models, namely GARCH and threshold GARCH models. Posterior evidence favouring the threshold GARCH model indicates threshold nonlinearity or volatility asymmetry. Simulation experiments demonstrate that our method works very well in distinguishing GARCH and threshold GARCH models. Sensitivity analysis shows that our method is robust to misspecification in error distribution. In the application to 10 market indexes, clear evidence of threshold nonlinearity is discovered and thus supporting volatility asymmetry. Copyright © 2005 John Wiley & Sons, Ltd. [source] An outlier robust GARCH model and forecasting volatility of exchange rate returnsJOURNAL OF FORECASTING, Issue 5 2002Beum-Jo Park Abstract Since volatility is perceived as an explicit measure of risk, financial economists have long been concerned with accurate measures and forecasts of future volatility and, undoubtedly, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used for doing so. It appears, however, from some empirical studies that the GARCH model tends to provide poor volatility forecasts in the presence of additive outliers. To overcome the forecasting limitation, this paper proposes a robust GARCH model (RGARCH) using least absolute deviation estimation and introduces a valuable estimation method from a practical point of view. Extensive Monte Carlo experiments substantiate our conjectures. As the magnitude of the outliers increases, the one-step-ahead forecasting performance of the RGARCH model has a more significant improvement in two forecast evaluation criteria over both the standard GARCH and random walk models. Strong evidence in favour of the RGARCH model over other competitive models is based on empirical application. By using a sample of two daily exchange rate series, we find that the out-of-sample volatility forecasts of the RGARCH model are apparently superior to those of other competitive models. Copyright © 2002 John Wiley & Sons, Ltd. [source] Quasi-maximum likelihood estimation of periodic GARCH and periodic ARMA-GARCH processesJOURNAL OF TIME SERIES ANALYSIS, Issue 1 2009Abdelhakim Aknouche Primary: 62F12; Secondary: 62M10, 91B84 Abstract., This article establishes the strong consistency and asymptotic normality (CAN) of the quasi-maximum likelihood estimator (QMLE) for generalized autoregressive conditionally heteroscedastic (GARCH) and autoregressive moving-average (ARMA)-GARCH processes with periodically time-varying parameters. We first give a necessary and sufficient condition for the existence of a strictly periodically stationary solution of the periodic GARCH (PGARCH) equation. As a result, it is shown that the moment of some positive order of the PGARCH solution is finite, under which we prove the strong consistency and asymptotic normality of the QMLE for a PGARCH process without any condition on its moments and for a periodic ARMA-GARCH (PARMA-PGARCH) under mild conditions. [source] Evaluating Specification Tests for Markov-Switching Time-Series ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2008Daniel R. Smith C12; C15; C22 Abstract., We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung,Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung,Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar,British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data. [source] Stability of nonlinear AR-GARCH modelsJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2008Mika Meitz Abstract., This article studies the stability of nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a nonlinear autoregression of order p [AR(p)] with the conditional variance specified as a nonlinear first-order generalized autoregressive conditional heteroskedasticity [GARCH(1,1)] model. Conditions under which the model is stable in the sense that its Markov chain representation is geometrically ergodic are provided. This implies the existence of an initial distribution such that the process is strictly stationary and , -mixing. Conditions under which the stationary distribution has finite moments are also given. The results cover several nonlinear specifications recently proposed for both the conditional mean and conditional variance, and only require mild moment conditions. [source] Contemporaneous aggregation of GARCH processesJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2007Paolo Zaffaroni Abstract., In this article, the effect of contemporaneous aggregation of heterogeneous generalized autoregressive conditionally heteroskedastic (GARCH) processes, as the cross-sectional size diverges to infinity is studied. We analyse both cases of cross-sectionally dependent and independent individual processes. The limit aggregate does not belong to the class of GARCH processes. Dynamic conditional heteroskedasticity is only preserved when the individual processes are sufficiently cross-correlated, although long memory for the limit aggregate volatility is not attainable. We also explore more general forms of cross-sectional dependence and various types of aggregation schemes. [source] A Dependence Metric for Possibly Nonlinear ProcessesJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2004C. W. Granger Abstract., A transformed metric entropy measure of dependence is studied which satisfies many desirable properties, including being a proper measure of distance. It is capable of good performance in identifying dependence even in possibly nonlinear time series, and is applicable for both continuous and discrete variables. A nonparametric kernel density implementation is considered here for many stylized models including linear and nonlinear MA, AR, GARCH, integrated series and chaotic dynamics. A related permutation test of independence is proposed and compared with several alternatives. [source] Prediction in ARMA Models with GARCH in Mean EffectsJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2001Menelaos Karanasos This paper considers forecasting the conditional mean and variance from an ARMA model with GARCH in mean effects. Expressions for the optimal predictors and their conditional and unconditional MSEs are presented. We also derive the formula for the covariance structure of the process and its conditional variance. JEL. C22. [source] Conditional Heteroskedasticity Driven by Hidden Markov ChainsJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2001Christian Francq We consider a generalized autoregressive conditionally heteroskedastic (GARCH) equation where the coefficients depend on the state of a nonobserved Markov chain. Necessary and sufficient conditions ensuring the existence of a stationary solution are given. In the case of ARCH regimes, the maximum likelihood estimates are shown to be consistent. The identification problem is also considered. This is illustrated by means of real and simulated data sets. [source] Exponential Tilting with Weak Instruments: Estimation and Testing,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2010Mehmet Caner Abstract This article analyses exponential tilting estimator with weak instruments in a nonlinear framework. Our paper differs from the previous literature in the context of consistency proof. Tests that are robust to the identification problem are also analysed. These are Anderson,Rubin and Kleibergen types of test statistics. We also conduct a simulation study wherein we compare empirical likelihood and continuous updating-based tests with exponential tilting (ET)-based ones. The designs involve GARCH(1,1) and contaminated structural errors. We find that ET-based Kleibergen test has the best size among these competitors. [source] Modelling Regime-Specific Stock Price Volatility,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 6 2009Carol Alexander Abstract Single-state generalized autoregressive conditional heteroscedasticity (GARCH) models identify only one mechanism governing the response of volatility to market shocks, and the conditional higher moments are constant, unless modelled explicitly. So they neither capture state-dependent behaviour of volatility nor explain why the equity index skew persists into long-dated options. Markov switching (MS) GARCH models specify several volatility states with endogenous conditional skewness and kurtosis; of these the simplest to estimate is normal mixture (NM) GARCH, which has constant state probabilities. We introduce a state-dependent leverage effect to NM-GARCH and thereby explain the observed characteristics of equity index returns and implied volatility skews, without resorting to time-varying volatility risk premia. An empirical study on European equity indices identifies two-state asymmetric NM-GARCH as the best fit of the 15 models considered. During stable markets volatility behaviour is broadly similar across all indices, but the crash probability and the behaviour of returns and volatility during a crash depends on the index. The volatility mean-reversion and leverage effects during crash markets are quite different from those in the stable regime. [source] Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic PerformanceOXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2006Stilianos Fountas Abstract We use a bivariate generalized autoregressive conditionally heteroskedastic (GARCH) model of inflation and output growth to examine the causality relationship among nominal uncertainty, real uncertainty and macroeconomic performance measured by the inflation and output growth rates. The application of the constant conditional correlation GARCH(1,1) model leads to a number of interesting conclusions. First, inflation does cause negative welfare effects, both directly and indirectly, i.e. via the inflation uncertainty channel. Secondly, in some countries, more inflation uncertainty provides an incentive to Central Banks to surprise the public by raising inflation unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic models, business cycle variability and the rate of economic growth are related. More variability in the business cycle leads to more output growth. [source] The Impact of Short- and Long-run Exchange Rate Uncertainty on Investment: A Panel Study of Industrial Countries,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2005Joseph P. Byrne Abstract We examine the relationship between aggregate investment and exchange rate uncertainty in the G7, using panel estimation and decomposition of volatility derived from the components generalized autoregressive conditionally heteroscedastic (GARCH) model. Our dynamic panel approach takes account of potential cross-sectional heterogeneity, which can lead to bias in estimation. We find that for a poolable subsample of European countries, it is the transitory and not the permanent component of volatility which adversely affects investment. To the extent that short-run uncertainty in the CGARCH model characterizes higher frequency shocks generated by volatile short-term capital flows, these are most deleterious for investment. [source] Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House PricesREAL ESTATE ECONOMICS, Issue 2 2003Gordon W. Crawford While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting. [source] | |||||||||||||||||||||||||||||||