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47
Bayesian Analysis of Stochastic Volatility Models
, 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener alized ARCH ..."
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Cited by 363 (20 self)
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this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener alized ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and logvolatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility found in many economic time series has fostered an explosion in the use of ARCH models. On the other hand, the likelihood function for stochastic volatility models is difficult to evaluate, and hence these models have had limited empirical application
Measuring and testing the impact of news on volatility
 Journal of Finance
, 1993
"... This paper introduces the News Impact Curve to measure how new information is incorporated into volatility estimates. A variety of new and existing ARCH models are compared and estimated with daily Japanese stock return data to determine the shape of the News Impact Curve. New diagnostic tests are p ..."
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Cited by 337 (10 self)
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This paper introduces the News Impact Curve to measure how new information is incorporated into volatility estimates. A variety of new and existing ARCH models are compared and estimated with daily Japanese stock return data to determine the shape of the News Impact Curve. New diagnostic tests are presented which emphasize the asymmetry of the volatility response to news. A partially nonparametric ARCH model is introduced to allow the data to estimate this shape. A comparison of this model with the existing models suggests that the best models are one by Glosten Jaganathan and Runkle (GJR) and Nelson's EGARCE. Similar results hold on a precrash sample period but are less strong.
Stock Prices and Volume
, 1990
"... We undertake a comprehensive investigation of price and volume comovement using daily New York Stock Exchange data from 1928 to 1987. We adjust the data to take into account wellknown calendar effects and longrun trends. To describt tbe process, we use a seminonparametric estimate of the joint de ..."
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Cited by 109 (9 self)
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We undertake a comprehensive investigation of price and volume comovement using daily New York Stock Exchange data from 1928 to 1987. We adjust the data to take into account wellknown calendar effects and longrun trends. To describt tbe process, we use a seminonparametric estimate of the joint density of current price change and volume conditional on past price changes and volume. Four empirical regularities are found: 1) positive correlation between conditional volatility and volume, 2) large price movements are followed by high volume, 3) conditioning on lagged volume substantially attenuates the "leverage " effect, and 4) after conditioning on lagged volume, there is a positive risk/return relation.
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
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Cited by 88 (14 self)
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Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method of sieves provides one way to tackle such complexities by optimizing an empirical criterion function over a sequence of approximating parameter spaces, called sieves, which are significantly less complex than the original parameter space. With different choices of criteria and sieves, the method of sieves is very flexible in estimating complicated econometric models. For example, it can simultaneously estimate the parametric and nonparametric components in seminonparametric models with or without constraints. It can easily incorporate prior information, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. This chapter describes estimation of seminonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
Estimation of Stochastic Volatility Models with Diagnostics
 Journal of Econometrics
, 1995
"... Efficient Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stoch ..."
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Cited by 80 (9 self)
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Efficient Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are "Semiparametric ARCH" and "Nonlinear Nonparametric". With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient. Corresponding author: George Tauchen, Duke University, Department of Economics, Social Science Building, Box 90097, Durham NC 277080097 USA, phone 19196601812, FAX 19196848974, email get@tauchen.econ.duke.edu. 0 1 Introduction The stochastic volatility model has been proposed as a descripti...
On Learning the Derivatives of an Unknown Mapping with Multilayer Feedforward Networks
, 1989
"... Daniel F. Mccaffrey, and Douglas W. Nychka for helpful discussions relating to Recently, multiple input, single output, single hidden layer, feedforward neural networks have been shown to be capable of approximating a nonlinear map and its partial derivatives. Specifically, neural nets have been sho ..."
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Cited by 64 (7 self)
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Daniel F. Mccaffrey, and Douglas W. Nychka for helpful discussions relating to Recently, multiple input, single output, single hidden layer, feedforward neural networks have been shown to be capable of approximating a nonlinear map and its partial derivatives. Specifically, neural nets have been shown to be dense in various Sobolev spaces (Hornik, Stinchcombe and White, 1989). Building upon this result, we show that a net can be trained so that the map and its derivatives are learned. Specifically, we use a result of Gallant (1987b) to show that least squares and similar estimates are strongly consistent in Sobolev norm provided the number of hidden units and the size of the training set increase together. We illustrate these results by an applic~tion to the inverse problem of chaotic dynamics: recovery of a nonlinear map from a time series of iterates. These results extend automatically to nets that embed the single hidden layer, feedforward network as a special case. 1.1 1.
Prediction in dynamic models with time dependent conditional heteroskedasticity, Working paper no
, 1990
"... This paper considers forecasting the conditional mean and variance from a singleequation dynamic model with autocorrelated disturbances following an ARMA process, and innovations with timedependent conditional heteroskedasticity as represented by a linear GARCH process. Expressions for the minimum ..."
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Cited by 43 (7 self)
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This paper considers forecasting the conditional mean and variance from a singleequation dynamic model with autocorrelated disturbances following an ARMA process, and innovations with timedependent conditional heteroskedasticity as represented by a linear GARCH process. Expressions for the minimum MSE predictor and the conditional MSE are presented. We also derive the formula for all the theoretical moments of the prediction error distribution from a general dynamic model with GARCHtl, 1) innovations. These results are then used in the construction of ex ante prediction confidence intervals by means of the CornishFisher asymptotic expansion. An empirical example relating to the uncertainty of the expected depreciation of foreign exchange rates illustrates the usefulness of the results. 1.
The Specification of Conditional Expectations
, 1991
"... this paper was written while the author was visiting the Graduate School of Business at the University of Chicago. This paper incorporates some results previously circulated in Is the Expected Compensation for Market Volatility Constant Through Time? and On the Linearity of Conditionally Expected ..."
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Cited by 43 (6 self)
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this paper was written while the author was visiting the Graduate School of Business at the University of Chicago. This paper incorporates some results previously circulated in Is the Expected Compensation for Market Volatility Constant Through Time? and On the Linearity of Conditionally Expected Returns. I have bene tted from the comments of Daniel Beneish, Marshall Blume, Doug Breeden, Wayne Ferson, Doug Foster, Mike Giarla, Mike Hemler, Ravi Jagannathan, Dan Nelson, Adrian Pagan, Tom Smith, Rob Stambaugh, S
A new approach to international arbitrage pricing
 Journal of Finance
, 1993
"... us, seminar participants at the 1992 Econometric Society summer meeting in ..."
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Cited by 38 (3 self)
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us, seminar participants at the 1992 Econometric Society summer meeting in
SNP: A program for nonparametric time series analysis. Version 8.7
, 1997
"... This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be usef ..."
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Cited by 37 (5 self)
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This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program;