scholarly journals TIME-VARYING COEFFICIENT MODELS: A PROPOSAL FOR SELECTING THE COEFFICIENT DRIVER SETS

2016 ◽  
Vol 21 (5) ◽  
pp. 1158-1174 ◽  
Author(s):  
Stephen G. Hall ◽  
P. A. V. B. Swamy ◽  
George S. Tavlas
Keyword(s):  
Arbitrary Element ◽  
The Other ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Sovereign Bond ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Bond Spreads

Coefficient drivers are observable variables that feed into time-varying coefficients (TVCs) and explain at least part of their movement. To implement the TVC approach, the drivers are split into two subsets, one of which is correlated with the bias-free coefficient that we want to estimate and the other with the misspecification in the model. This split, however, can appear to be arbitrary. We provide a way of splitting the drivers that takes account of any nonlinearity that may be present in the data, with the aim of removing the arbitrary element in driver selection. We also provide an example of the practical use of our method by applying it to modeling the effect of ratings on sovereign-bond spreads.

scholarly journals Forecasting Volatility with Time-Varying Coefficient Regressions

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qifeng Zhu ◽  
Miman You ◽  
Shan Wu
Keyword(s):  
Constant Coefficient ◽  
Stock Exchange ◽  
Composite Index ◽  
Predictive Ability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Out Of Sample ◽  
Time Varying Coefficients

We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

scholarly journals Time-Varying Coefficient Models And The Kalman Filter : Applications To Hedge Funds

2021 ◽  
Author(s):  
Ana G.S. Punales
Keyword(s):  
Kalman Filter ◽  
Hedge Funds ◽  
Hedge Fund ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Likelihood Estimator ◽  
Varying Coefficients ◽  
Industry Performance ◽  
Non Linear ◽  
Time Varying Coefficients

There are various studies concerned with the estimation of stochastically varying coefficients for the hedge fund series but just [sic] few are available in the literature that study the model with time-varying coefficients and non-linear factor, or make a comparison of the series before and during the financial crisis. This work studies a model with linear and non-linear factors with stochastically varying coefficients to obtain better estimation of the exposure of the hedge fund and accuracy in the results. Better exposure estimates implies better hedging against negative changes in the market hence a reduction in the risk taken by the hedge fund manager. Besides, different techniques have been studied, implemented and applied in this thesis to estimate and analyze time varying exposures of different HFRX Index (an index that describes the hedge fund industry performance). The study shows that option-like models with time-varying coefficients perform the best for most of the HFRX indexes analyzed. It also shows that the Kalman Filter technique combined with the Maximum Likelihood Estimator is the best approach to estimate time-varying coefficients. In addition, we provide evidence that Kalman Filter is in a better position to capture changes in the exposure to the market conditions.

scholarly journals Wavelet-M-Estimation for Time-Varying Coefficient Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xingcai Zhou ◽  
Fangxia Zhu
Keyword(s):  
Time Series ◽  
Asymptotic Properties ◽  
Time Series Models ◽  
Time Varying ◽  
Bahadur Representation ◽  
Conditional Quantile ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
M Estimation ◽  
Time Varying Coefficients

This paper proposes wavelet-M-estimation for time-varying coefficient time series models by using a robust-type wavelet technique, which can adapt to local features of the time-varying coefficients and does not require the smoothness of the unknown time-varying coefficient. The wavelet-M-estimation has the desired asymptotic properties and can be used to estimate conditional quantile and to robustify the usual mean regression. Under mild assumptions, the Bahadur representation and the asymptotic normality of wavelet-M-estimation are established.

Analytical Insights into a Generalized Semidiscrete System with Time-Varying Coefficients: Derivation, Exact Solutions, and Nonlinear Soliton Dynamics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Sheng Zhang ◽  
Sen Zhao ◽  
Bo Xu
Keyword(s):  
Inverse Scattering ◽  
Soliton Solutions ◽  
Time Varying ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Soliton Dynamics ◽  
Special Cases ◽  
Time Varying Coefficients ◽  
Matrix Spectral Problem ◽  
Propagation Velocities

In this paper, a new generalized semidiscrete integrable system with time-varying coefficients is analytically studied. Firstly, the generalized semidiscrete system is derived from a semidiscrete matrix spectral problem by embedding finite time-varying coefficient functions. Secondly, exact and explicit N-soliton solutions of the semidiscrete system are obtained by using the inverse scattering analysis. Finally, three special cases when N=1,2,3 of the obtained N-soliton solutions are simulated by selecting some appropriate coefficient functions. It is shown that the time-varying coefficient functions affect the spatiotemporal structures and the propagation velocities of the obtained semidiscrete one-soliton solutions, two-soliton solutions, and three-soliton solutions.

scholarly journals Time-Varying Coefficient Models And The Kalman Filter : Applications To Hedge Funds

2021 ◽  
Author(s):  
Ana G.S. Punales
Keyword(s):  
Kalman Filter ◽  
Hedge Funds ◽  
Hedge Fund ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Likelihood Estimator ◽  
Varying Coefficients ◽  
Industry Performance ◽  
Non Linear ◽  
Time Varying Coefficients

There are various studies concerned with the estimation of stochastically varying coefficients for the hedge fund series but just [sic] few are available in the literature that study the model with time-varying coefficients and non-linear factor, or make a comparison of the series before and during the financial crisis. This work studies a model with linear and non-linear factors with stochastically varying coefficients to obtain better estimation of the exposure of the hedge fund and accuracy in the results. Better exposure estimates implies better hedging against negative changes in the market hence a reduction in the risk taken by the hedge fund manager. Besides, different techniques have been studied, implemented and applied in this thesis to estimate and analyze time varying exposures of different HFRX Index (an index that describes the hedge fund industry performance). The study shows that option-like models with time-varying coefficients perform the best for most of the HFRX indexes analyzed. It also shows that the Kalman Filter technique combined with the Maximum Likelihood Estimator is the best approach to estimate time-varying coefficients. In addition, we provide evidence that Kalman Filter is in a better position to capture changes in the exposure to the market conditions.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

A NOTE ON MUTH'S RATIONAL EXPECTATIONS HYPOTHESIS: A TIME-VARYING COEFFICIENT INTERPRETATION

2006 ◽  
Vol 10 (3) ◽  
pp. 415-425 ◽  
Author(s):  
P.A.V.B. SWAMY ◽  
GEORGE S. TAVLAS
Keyword(s):  
Rational Expectations ◽  
Econometric Model ◽  
Objective Probability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Exact Representation ◽  
Expectations Hypothesis ◽  
Varying Coefficient ◽  
True Model ◽  
Necessary And Sufficient

Under certain interpretations of its coefficients, a specified econometric model is an exact representation of the “true” model, defining the “objective” probability distribution. This note enumerates these interpretations. In the absence of the conditions implied by these interpretations, the econometric model is misspecified. The note shows that model misspecifications prevent the satisfaction of a necessary and sufficient condition for individual expectations to be rational in Muth's sense. Whereas restrictive forms of econometric models can give very inaccurate predictions, this note describes the conditions under which the predictions generated from time-varying coefficient models coincide with the predictions generated from the relevant economic theory.

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