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Nonlinearity, Nonstationarity, and Thick Tails: How They Interact to Generate Persistency in Memory 1
"... We consider nonlinear transformations of random walks driven by thicktailed innovations that may have infinite means or variances. These three nonstandard characteristics: nonlinearity, nonstationarity, and thick tails interact to generate a spectrum of asymptotic autocorrelation patterns consisten ..."
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Cited by 8 (4 self)
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We consider nonlinear transformations of random walks driven by thicktailed innovations that may have infinite means or variances. These three nonstandard characteristics: nonlinearity, nonstationarity, and thick tails interact to generate a spectrum of asymptotic autocorrelation patterns consistent with longmemory processes. Such autocorrelations may decay very slowly as the number of lags increases or may not decay at all and remain constant at all lags. Depending upon the type of transformation considered and how the model error is specified, the autocorrelation functions are given by random constants, deterministic functions that decay slowly at hyperbolic rates, or mixtures of the two. Such patterns, along with other sample characteristics of the transformed time series, such as jumps in the sample path, excessive volatility, and leptokurtosis, suggest the possibility that these three ingredients are involved in the data generating processes of many actual economic and financial time series data. In addition to time series characteristics, we explore nonlinear regression asymptotics when the regressor is observable and an alternative regression technique when it is unobservable. To illustrate, we examine two empirical applications: wholesale electricity price spikes driven by capacity shortfalls and exchange rates governed by a target zone.
Bayesian Hypothesis Testing in Latent Variable Models
, 2010
"... Hypothesis testing using Bayes factors (BFs) is known to suffer from several problems in the context of latent variable models. The first problem is computational. Another problem is that BFs are not well defined under the improper prior. In this paper, a new Bayesian method, based on decision theo ..."
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Cited by 2 (1 self)
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Hypothesis testing using Bayes factors (BFs) is known to suffer from several problems in the context of latent variable models. The first problem is computational. Another problem is that BFs are not well defined under the improper prior. In this paper, a new Bayesian method, based on decision theory and the EM algorithm, is introduced to test a point hypothesis in latent variable models. The new statistic is a byproduct of the Bayesian MCMC output and, hence, easy to compute. It is shown that the new statistic is appropriately defined under improper priors because the method employs a continuous loss function. The finite sample properties are examined using simulated data. The method is also illustrated in the context of a onefactor asset pricing model and a stochastic volatility model with jumps using real data.
Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error
"... We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies, or have other irregularities that cause the econometrician to observe them with mildly nonstationary noise. Least s ..."
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Cited by 1 (0 self)
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We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies, or have other irregularities that cause the econometrician to observe them with mildly nonstationary noise. Least squares estimation of the cointegrating vector is consistent. Existing prototypical variancebased estimation techniques, such as canonical cointegrating regression (CCR), are both consistent and asymptotically mixed normal. This result is robust to weakly dependent but possibly nonstationary disturbances.
and Deutsche Forschungsgemeinschaft through the SFB 649 Economic Risk. We would like to thank
, 2013
"... applications to the fine art market ..."
ANY OPINIONS EXPRESSED ARE THOSE OF THE AUTHOR(S) AND NOT NECESSARILY THOSE OF THE SCHOOL OF ECONOMICS, SMUBayesian Learning of Impacts of SelfExciting Jumps
, 2012
"... The paper proposes a new class of continuoustime asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset returns, it is simultaneously passed on to diffusion variance and the jump intensity, generating selfexciting cojumps of prices and volatility ..."
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The paper proposes a new class of continuoustime asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset returns, it is simultaneously passed on to diffusion variance and the jump intensity, generating selfexciting cojumps of prices and volatility and jump clustering. To properly deal with parameter uncertainty and insample overfitting, a Bayesian learning approach combined with an efficient particle filter is employed. It not only allows for comparison of both nested and nonnested models, but also generates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows that volatility jumps at the same time as negative jumps in asset returns mainly through jumps in diffusion volatility. We find substantial evidence for jump clustering, in particular, after the recent financial crisis in 2008, even though parameters driving dynamics of the jump intensity
GARCH(1,1) Process with Persistent Covariates
, 2007
"... We consider a model called GARCHNNH, which is a GARCH(1,1) process with a nonlinear function of a persistent, integrated or nearly integrated, variable. We derive the asymptotic distribution theory of the quasimaximum likelihood estimator in the GARCHNNH model. We establish the consistency and as ..."
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We consider a model called GARCHNNH, which is a GARCH(1,1) process with a nonlinear function of a persistent, integrated or nearly integrated, variable. We derive the asymptotic distribution theory of the quasimaximum likelihood estimator in the GARCHNNH model. We establish the consistency and asymptotic mixed normality of the quasimaximum likelihood estimator in the GARCHNNH model. Next, we investigate how the GARCHNNH model explains stylized facts about volatility in …nancial return series such as the long memory property in volatility, leptokurtosis and the IGARCH behavior.
Trend Filtering Methods for Momentum Strategies ∗
, 2011
"... This paper studies trend filtering methods. These methods are widely used in momentum strategies, which correspond to an investment style based only on the history of past prices. For example, the CTA strategy used by hedge funds is one of the bestknown momentum strategies. In this paper, we review ..."
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This paper studies trend filtering methods. These methods are widely used in momentum strategies, which correspond to an investment style based only on the history of past prices. For example, the CTA strategy used by hedge funds is one of the bestknown momentum strategies. In this paper, we review the different econometric estimators to extract a trend of a time series. We distinguish between linear and nonlinear models as well as univariate and multivariate filtering. For each approach, we provide a comprehensive presentation, an overview of its advantages and disadvantages and an application to the S&P 500 index. We also consider the calibration problem of these filters. We illustrate the two main solutions, the first based on prediction error, and the second using a benchmark estimator. We conclude the paper by listing some issues to consider when implementing a momentum strategy.
papers/ Measuring the EuroDollar Permanent Equilibrium Exchange Rate using the Unobserved Components Model1
, 2014
"... We would like to thank the Editor and two anonymous referees for their helpful and constructive comments on a previous draft of this paper. ..."
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We would like to thank the Editor and two anonymous referees for their helpful and constructive comments on a previous draft of this paper.
Testing the Bounds: Empirical Behavior of Target Zone Fundamentals
"... Standard target zone exchange rate models are based on nonlinear functions of unobserved economic fundamentals, which are assumed to be bounded, similarly to the target zone exchange rates themselves. Using a novel estimation and testing strategy, I show how this key but often overlooked assumption ..."
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Standard target zone exchange rate models are based on nonlinear functions of unobserved economic fundamentals, which are assumed to be bounded, similarly to the target zone exchange rates themselves. Using a novel estimation and testing strategy, I show how this key but often overlooked assumption may be tested. Empirical results cast doubt on its validity in practice, providing a reason for welldocumented empirical difficulties of these models in the literature.