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The Nobel Memorial Prize for Robert F. Engle
01 Feb 2004-Research Papers in Economics (Penn Institute for Economic Research, Department of Economics, University of Pennsylvania)-
TL;DR: Engle's contributions include early work on band-spectral regression, development and unification of the theory of model specification tests (particularly Lagrange multiplier tests), clarification of the meaning of econometric exogeneity and its relationship to causality, and later stunningly influential work on common trend modeling (cointegration) and volatility modelling (ARCH, short for Auto Regressive Conditional Heteroskedasticity).
Abstract: Engle’s footsteps range widely. His major contributions include early work on band-spectral regression, development and unification of the theory of model specification tests (particularly Lagrange multiplier tests), clarification of the meaning of econometric exogeneity and its relationship to causality, and his later stunningly influential work on common trend modeling (cointegration) and volatility modelling (ARCH, short for Auto Regressive Conditional Heteroskedasticity). More generally, Engle’s cumulative work is a fine example of best-practice applied time-series econometrics: he identifies important dynamic economic phenomena, formulates precise and interesting questions about those phenomena, constructs sophisticated yet simple econometric models for measurement and testing, and consistently obtains results of widespread substantive interest in the scientific, policy, and financial communities.
Citations
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TL;DR: In this article, a computer program for modelling financial time series is presented, based on the Random Walk Hypothesis, which is used to forecast trends in prices in futures markets.
Abstract: Features of Financial Returns Modelling Price Volatility Forecasting Standard Deviations The Accuracy of Autocorrelation Estimates Testing the Random Walk Hypothesis Forecasting Trends in Prices Evidence Against the Efficiency of Futures Markets Valuing Options Appendix: A Computer Program for Modelling Financial Time Series.
1,115 citations
01 Jan 1994
TL;DR: In this paper, a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature is presented, presenting a comprehensive review of both theoretical and applied concepts.
Abstract: This paper provides a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature, presenting a comprehensive review of both theoretical and applied concepts. Perhaps the most novel feature of the paper is its use of Kalman filtering together with econometric and time series methodology. From a technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models.
22 citations
03 Jun 2019
TL;DR: In this paper, analisis volatilitas harga bawang merah dalam penelitian ini dilakukan dengan bantuan software Eviews 6.
Abstract: Analisis mengenai volatilitas harga sangat diperlukan terutama untuk menentukan kebijakan mengenai harga di masa yang akan datang. salah satu alat analisis yang biasa digunakan untuk menganalisis sifat volatilitas harga adalah ARCH/GARCH. Penelitian ini bertujuan untuk menganasis sifat volatilitas harga bawang merah nasional menggunakan ARCH/GARCH. Data yang digunakan dalam penelitian ini adalah data sekunder yaitu harga rata-rata harian bawang merah nasional dalam kurun waktu 2011-2015. Analisis volatilitas harga bawang merah dalam penelitian ini dilakukan dengan bantuan software Eviews 6. Hasil penelitian menunjukan bahwa karakteristik volatilitas harga bawang merah dalam kurun waktu 2011-2015 berdasarkan model ARCH/GARCH dikategorikan rendah sehingga pergerakan harga dapat diprediksi dan diantisipasi sebagai early warning system akan terjadinya lonjakan atau penurunan harga. Selain itu dapat diestimasi bahwa volatilitas harga bawang di masa datang akan semakin kecil dengan perubahan harga harian bawang merah terjadi rata-rata setiap enam hari. Oleh karena hal tersebut disarankan bagi pengambil kebijakan untuk mengatur distribusi ketika harga mulai bergejolak untuk menghindari kenaikan harga yang lebih tinggi atau jatuh.
7 citations
TL;DR: In this article, the authors analyzed the characteristics of cocoa price movement in cocoa futures trading, and analyzed the cocoa price volatility using ARCH and GARCH type model, and showed that GARCH is the best model to predict the value of average cocoa price return volatility, because it meets criteria of three diagnostic checking, which are ARCH-LM test, residual autocorrelation test and residual normality test.
Abstract: Dynamics of market changing as a result of market liberalization have an impact on agricultural commodities price fluctuation. High volatility on cocoa price movement reflect its price and market risk. Because of price and market uncertainty, the market players face some difficulties to make a decision in determining business development. This research was conducted to 1) understand the characteristics of cocoa price movement in cocoa futures trading, and 2)analyze cocoa price volatility using ARCH and GARCH type model. Research was carried out by direct observation on the pattern of cocoa price movement in the futures trading and volatility analysis based on secondary data. The data was derived from Intercontinental Exchange ( ICE) Futures U.S. Reports. The analysis result showed that GARCH is the best model to predict the value of average cocoa price return volatility, because it meets criteria of three diagnostic checking, which are ARCH-LM test, residual autocorrelation test and residual normality test. Based on the ARCH-LM test, GARCH (1,1)did not have heteroscedasticity, because p-value 2 (0.640139)and F-statistic (0.640449) were greater than 0.05. Results of residual autocorrelation test indicated that residual value of GARCH (1,1) was random, because the statistic value of Ljung-Box (LB)on the 36 th lag is smaller than the statistic value of 2. Whereas, residual normality test concluded the residual of GARCH (1,1) were normally distributed, because AR (29), MA (29), RESID (-1)^2, and GARCH (-1) were significant at 5% significance level. Increasing volatility value indicate high potential risk. Price risk can be reduced by managing financial instrument in futures trading such as forward and futures contract, and hedging. The research result also give an insight to the market player for decision making and determining time of hedging. Key words: Volatility, price, cocoa, GARCH, risk, futures trading
5 citations
References
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TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Abstract: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.
27,170 citations
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Abstract: Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period forecast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals. This model is used to estimate the means and variances of inflation in the U.K. The ARCH effect is found to be significant and the estimated variances increase substantially during the chaotic seventies.
20,728 citations
8,252 citations
TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.
Abstract: Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.
5,695 citations
TL;DR: In this paper, a new parameterization of the multivariate ARCH process is proposed and equivalence relations are discussed for the various ARCH parameterizations, and conditions suffcient to guarantee the positive deffniteness of the covariance matrices are developed.
Abstract: This paper presents theoretical results in the formulation and estimation of multivariate gen- eralized ARCH models within simultaneous equations systems. A new parameterization of the multivariate ARCH process is proposed and equivalence relations are discussed for the various ARCH parameterizations. Constraints suffcient to guarantee the positive deffniteness of the con- ditional covariance matrices are developed, and necessary and suffcient conditions for covariance stationarity are presented. Identifcation and maximum likelihood estimation of the parameters in the simultaneous equations context are also covered.
4,413 citations