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29
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.
Is default event risk priced in corporate bonds. Working
, 2002
"... We identify and estimate the sources of risk that cause corporate bonds to earn an excess return over defaultfree bonds. In particular, we estimate the risk premium associated with a default event. Default is modelled using a jump process with stochastic intensity. For a large set of firms, we mode ..."
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Cited by 86 (1 self)
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We identify and estimate the sources of risk that cause corporate bonds to earn an excess return over defaultfree bonds. In particular, we estimate the risk premium associated with a default event. Default is modelled using a jump process with stochastic intensity. For a large set of firms, we model the default intensity of each firm as a function of common and firmspecific factors. In the model, corporate bond excess returns can be due to risk premia on factors driving the intensities and due to a risk premium on the default jump risk. The model is estimated using data on corporate bond prices for 104 US firms and historical default rate data. We find significant risk premia on the factors that drive intensities. However, these risk premia cannot fully explain the size of corporate bond excess returns. Next, we estimate the size of the default jump risk premium, correcting for possible tax and liquidity effects. The estimates show that this event risk premium is a significant and economically important determinant of excess corporate bond returns.
Nonlinear Dynamic Structures
 Econometrica
, 1993
"... We describe three methods for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its onestep ahead conditional density. These strategies are based on examination of conditional moment profiles corresponding to certain shocks; a conditional moment pr ..."
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Cited by 84 (10 self)
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We describe three methods for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its onestep ahead conditional density. These strategies are based on examination of conditional moment profiles corresponding to certain shocks; a conditional moment profile is the conditional expectation evaluated at time t of a time invariant function evaluated at time t + j regarded as a function of j. The first method, which compares conditional moment profiles to baseline profiles, is the nonlinear analog of conventional impulseresponse analysis. The second assesses the significance of a profile by comparing its supnorm confidence band to a null profile. The third examines profile bundles for evidence of damping or persistence. Experimental designs for choosing an appropriate set of shocks are discussed. These methods are applied to a bivariate series comprised of daily changes in the Standard and Poor's composite price index and daily NYSE transactions volume from 1928 to 1987. The findings from these data are: (i) The multistep ahead conditional volatility profile exhibits a symmetric response to both positive and negative price shocks. In contrast, the conditional volatility profile of the univariate price change process exhibits an asymmetric response. (ii) The onestep ahead response of volume to price shocks is different than the multistep ahead response. Price shocks produce an increase in volume onestep ahead but decrease it in subsequent steps. (iii) There is little evidence for longterm persistence in either the conditional mean or volatility of the bivariate process. o 1
Reprojecting Partially Observed Systems with Application to Interest Rate Diffusions from January 5, 1992, to March 31, 1995
, 1996
"... We introduce reprojection as a general purpose technique for characterizing the observable dynamics of a partially observed nonlinear system. System parameters are estimated by method of moments wherein moments implied by the system are matched to moments implied by the transition density for observ ..."
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Cited by 81 (13 self)
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We introduce reprojection as a general purpose technique for characterizing the observable dynamics of a partially observed nonlinear system. System parameters are estimated by method of moments wherein moments implied by the system are matched to moments implied by the transition density for observables that is determined by projecting the data onto its Hermite representation. Reprojection imposes the constraints implied by the system on the transition density and is accomplished by projecting a long simulation of the estimated system onto the Hermite representation. We utilize the technique to assess the dynamics of several diffusion models for the shortterm interest rate that have been proposed and compare them to a new model that has feedback from the interest rate into both the drift and diffusion coefficients of a volatility equation. This effort entails the development of new graphical diagnostics.
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...
Term Structure of Interest Rates with Regime Shifts
 Journal of Finance
, 2002
"... We develop a term structure model where the short interest rate and the market price of risks are subject to discrete regime shifts. Empirical evidence from efficient method of moments estimation provides considerable support for the regime shifts model. Standard models, which include affine specifi ..."
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Cited by 79 (1 self)
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We develop a term structure model where the short interest rate and the market price of risks are subject to discrete regime shifts. Empirical evidence from efficient method of moments estimation provides considerable support for the regime shifts model. Standard models, which include affine specifications with up to three factors, are sharply rejected in the data. Our diagnostics show that only the regime shifts model can account for the welldocumented violations of the expectations hypothesis, the observed conditional volatility, and the conditional correlation across yields. We find that regimes are intimately related to business cycles. MANY PAPERS DOCUMENT THAT THE UNIVARIATE short interest rate process can be reasonably well modeled in the time series as a regime switching process ~see Hamilton ~1988!, Garcia and Perron ~1996!!. In addition to this statistical evidence, there are economic reasons as well to believe that regime shifts are important to understanding the behavior of the entire yield curve. For example, business cycle expansion and contraction “regimes ” potentially
Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance
, 1999
"... A common model for security price dynamics is the continuous time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the BlackScholes price with the forward integrated variance replacing the BlackScholes va ..."
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Cited by 64 (3 self)
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A common model for security price dynamics is the continuous time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the BlackScholes price with the forward integrated variance replacing the BlackScholes variance. Implementing the Hull and White characterization requires both estimates of the price dynamics and the conditional distribution of the forward integrated variance given observed variables. Using daily data on closetoclose price movement and the daily range, we find that standard models do not fit the data very well and a more general three factor model does better, as it mimics the longmemory feature of financial volatility. We develop techniques for estimating the conditional distribution of the forward integrated variance given observed variables. 1 Introduction This paper has two objectives: The first is to extend and implement methods for estimating diffusion models of secu...
Finding Chaos in Noisy Systems
, 1991
"... In the past twenty years there has been much interest in the physical and biological sciences in nonlinear dynamical systems that appear to have random, unpredictable behavior. One important parameter of a dynamic system is the dominant Lyapunov exponent (LE). When the behavior of the system is comp ..."
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Cited by 50 (1 self)
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In the past twenty years there has been much interest in the physical and biological sciences in nonlinear dynamical systems that appear to have random, unpredictable behavior. One important parameter of a dynamic system is the dominant Lyapunov exponent (LE). When the behavior of the system is compared for two similar initial conditions, this exponent is related to the rate at which the subsequent trajectories diverge. A bounded system with a positive LE is one operational definition of chaotic behavior. Most methods for determining the LE have assumed thousands of observations generated from carefully controlled physical experiments. Less attention has been given to estimating the LE for biological and economic systems that are subjected to random perturbations and observed over a limited amount of time. Using nonparametric regression techniques (Neural Networks and Thin Plate Splines) it is possible to consistently estimate the LE. The properties of these methods have been studied using simulated data and are applied to a biological time series: marten fur returns for the Hudson Bay Company (18201900). Based on a nonparametric analysis there is little evidence for lowdimensional chaos in these data. Although these methods appear to work well for systems perturbed by small amounts of noise, finding chaos in a system with a significant stochastic component may be difficult.
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;