Results 1  10
of
16
Nonlinear and NonGaussian StateSpace Modeling with Monte Carlo Techniques: A Survey and Comparative Study
 In Rao, C., & Shanbhag, D. (Eds.), Handbook of Statistics
, 2000
"... Since Kitagawa (1987) and Kramer and Sorenson (1988) proposed the filter and smoother using numerical integration, nonlinear and/or nonGaussian state estimation problems have been developed. Numerical integration becomes extremely computerintensive in the higher dimensional cases of the state vect ..."
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Cited by 18 (4 self)
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Since Kitagawa (1987) and Kramer and Sorenson (1988) proposed the filter and smoother using numerical integration, nonlinear and/or nonGaussian state estimation problems have been developed. Numerical integration becomes extremely computerintensive in the higher dimensional cases of the state vector. Therefore, to improve the above problem, the sampling techniques such as Monte Carlo integration with importance sampling, resampling, rejection sampling, Markov chain Monte Carlo and so on are utilized, which can be easily applied to multidimensional cases. Thus, in the last decade, several kinds of nonlinear and nonGaussian filters and smoothers have been proposed using various computational techniques. The objective of this paper is to introduce the nonlinear and nonGaussian filters and smoothers which can be applied to any nonlinear and/or nonGaussian cases. Moreover, by Monte Carlo studies, each procedure is compared by the root mean square error criterion.
Recursive Solution Methods for Dynamic Linear Rational Expectations Models
 Journal of Econometrics
, 1989
"... This paper develops recursive solution methods for linear rational expectations models. The underlying structural model is transformed into a statespace representation, which can then be used to solve the model and to form the Gaussian likelihood function. The recursive solution method has several ..."
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Cited by 8 (4 self)
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This paper develops recursive solution methods for linear rational expectations models. The underlying structural model is transformed into a statespace representation, which can then be used to solve the model and to form the Gaussian likelihood function. The recursive solution method has several advantages over other approaches. First, the set of solutions to the model are summarized by a set of parameters that appear in the statespace representation but are unspecified by the structural model. Next, the likelihood function is formed as a byproduct of the solution to the model. Finally, modifications in the likelihood function necessary to incorporate complications arising from temporal aggregation, dynamic errorsinvariables, etc. are straightforward. 1.
Modelling and Forecasting Exchange Rates with a Bayesian TimeVarying Coe¢ cient Model,”Journal of Economic Dynamics and Control
, 1993
"... This paper employs a multivariate Bayesian timevarying coetficients (TVC) approach to model and forecast exchange rate data. It is shown that, if used as a datagenerating mechanism, a TVC model induces nonlinearities in the conditional moments and leptokurtosis n the unconditional distribution of ..."
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Cited by 6 (0 self)
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This paper employs a multivariate Bayesian timevarying coetficients (TVC) approach to model and forecast exchange rate data. It is shown that, if used as a datagenerating mechanism, a TVC model induces nonlinearities in the conditional moments and leptokurtosis n the unconditional distribution of the series. It is also shown that leptokurtic behavior disappears under time aggregation. As a forecasting device, a Bayesian TVC model improves over a random walk model. The improvements are robust to several changes in the forecasting environment. I.
TimeVariation and Structural Change in the Forward Discount: Implications for the Forward Rate Unbiasedness Hypothesis
, 2005
"... It is a well accepted empirical result that forward exchange rate unbiasedness is rejected in tests using the “differences regression ” of the change in the logarithm of the spot exchange rate on the forward discount. The result is referred to in the International Finance literature as the forward d ..."
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Cited by 4 (0 self)
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It is a well accepted empirical result that forward exchange rate unbiasedness is rejected in tests using the “differences regression ” of the change in the logarithm of the spot exchange rate on the forward discount. The result is referred to in the International Finance literature as the forward discount puzzle. Competing explanations of the negative bias of the forward discount coefficient include the possibilities of a timevarying risk premium or the existence of “peso problems. ” We offer an alternative explanation for this anomaly. One of the stylized facts about the forward discount is that it is highly persistent. We model the forward discount as an AR(1) process and argue that its persistence is exaggerated due to the presence of structural breaks. We document the temporal variation in persistence, using a timevarying parameter specification for the AR(1) model, with Markovswitching disturbances. We also show, using a stochastic multiple break model, suggested recently by Bai and Perron (1998), that for the G7 countries, with the exception of Japan, the forward discount persistence is substantially less, if one allows for multiple structural breaks in the mean of the process. These breaks could be identified as monetary shocks to the central bank’s reaction function, as discussed in Eichenbaum and Evans (1995). Using Monte Carlo simulations we show that if we do not account for structural breaks which are present in the forward discount process, the forward discount coefficient in the “differences regression ” is severely biased downward, away from its true value of 1. The authors would like to thank Charles Engel and the participants of the macroeconomics seminar at the New York Federal Reserve Bank for helpful comments and suggestions and Jushan Bai for generously providing the GAUSS code to estimate the multiple break models. The usual disclaimer applies
Does an Intertemporal Tradeoff between Risk and Return Explain Mean Reversion in Stock Prices?
, 2000
"... : When volatility feedback is taken into account, there is strong evidence of a positive tradeoff between stock market volatility and expected returns on a market portfolio. In this paper, we ask whether this intertemporal tradeoff between risk and return is responsible for the reported evidence ..."
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Cited by 3 (0 self)
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: When volatility feedback is taken into account, there is strong evidence of a positive tradeoff between stock market volatility and expected returns on a market portfolio. In this paper, we ask whether this intertemporal tradeoff between risk and return is responsible for the reported evidence of mean reversion in stock prices. There are two relevant findings. First, price movements not related to the effects of Markovswitching market volatility are largely unpredictable over long horizons. Second, timevarying parameter estimates of the longhorizon predictability of stock returns reject any inherent mean reversion in favour of behaviour implicit in the historical tradeoff between risk and return. JEL classification: G12; G14 Keywords: Volatility Feedback; Mean Reversion; Markov Switching; TimeVarying Parameter 1 1. Introduction More than a decade has passed since Fama and French (1988) and Poterba and Summers (1988) reported that price movements for market portfolios...
ARCH Models for Multiperiod Forecast Uncertainty  A Reality Check Using a Panel of Density Forecasts
 ECONOMETRIC ANALYSIS OF FINANCIAL AND ECONOMIC TIME SERIES – PART A (EDS. D. TERRELL AND T.B. FOMBY), ELSEVIER, JAI.
"... We develop a theoretical model to compare forecast uncertainty estimated from time series models to those available from survey density forecasts. The sum of the average variance of individual densities and the disagreement is shown to approximate the predictive uncertainty from wellspecified time ..."
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Cited by 1 (0 self)
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We develop a theoretical model to compare forecast uncertainty estimated from time series models to those available from survey density forecasts. The sum of the average variance of individual densities and the disagreement is shown to approximate the predictive uncertainty from wellspecified time series models when the variance of the aggregate shocks is relatively small compared to that of the idiosyncratic shocks. Due to grouping error problems and compositional heterogeneity in the panel, individual densities are used to estimate aggregate forecast uncertainty. During periods of regime change and structural break, ARCH estimates tend to diverge from survey measures.
The View from Implicit Feedback Rules FREQUENTLY CITED THEORETICAL
"... framework for the conduct of monetary policy consists of a policy instrument, an intermediate policy target and a longrun policy objective. The policy instrument is a lever which the central bank can manipulate to achieve its intermediate target. Possible choices for the policy instrument include t ..."
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framework for the conduct of monetary policy consists of a policy instrument, an intermediate policy target and a longrun policy objective. The policy instrument is a lever which the central bank can manipulate to achieve its intermediate target. Possible choices for the policy instrument include the quantity of bank reserves, the monetary base (hank reserves plus currency in circulation) or a shortterm interest rate. Monetary policymakers aim at a value of the intermediate target variable that will make current monetary policy consistent with a longrun policy objective, such as price stability.1 Potential intermediate target variables include nominal gross domestic product (GUP) and monetary
“The Nobel Memorial Prize for Robert F. Engle” by
, 2004
"... Engle’s footsteps range widely. His major contributions include early work on bandspectral 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 caus ..."
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Engle’s footsteps range widely. His major contributions include early work on bandspectral 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 modeling (ARCH, short for AutoRegressive Conditional Heteroskedasticity). 2 More generally, Engle’s cumulative work is a fine example of bestpractice applied timeseries 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. Although many of Engle’s contributions are fundamental, I will focus largely on the two most important: the theory and application of cointegration, and the theory and application of dynamic volatility models. Moreover, I will discuss much more extensively Engle’s volatility models and their role in financial econometrics, for several reasons. First, Engle’s Nobel citation was explicitly “for methods of analyzing economic time series with timevarying volatility (ARCH), ” whereas Granger’s was for “for methods of analyzing economic time series with common trends (cointegration). ” Second, the credit for 1 Prepared at the invitation of the Scandinavian Journal of Economics. Financial support from the
Special Issue on Data Mining in Finance c○World Scientific Publishing Company A CONSTRAINED NEURAL NETWORK KALMAN FILTER FOR PRICE ESTIMATION IN HIGH FREQUENCY FINANCIAL DATA
"... In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the f ..."
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In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly observed in financial data: process noise, measurement noise, and arrival noise. The erratic arrival of data (arrival noise) results in the neural network predictions being iterated into the future. Constraining the neural network to have a fixed point at the origin produces better iterated predictions and more stable results. The performance of constrained and unconstrained neural networks within the extended Kalman filter is demonstrated on “Quote ” tick data from the $/DM exchange rate (1993–1995). 1.