Vector-valued multiple regression model with time varying coefficients and its application to predict excess stock returns

2002 ◽  
Author(s):  
Y. Kawasaki ◽  
S. Sato ◽  
S. Tachiki
Keyword(s):  
Regression Model ◽  
Stock Returns ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Time Varying ◽  
Varying Coefficients ◽  
Excess Stock Returns ◽  
Time Varying Coefficients ◽  
Vector Valued

scholarly journals Stock returns and liquidity risk in Chile

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Francisco Javier Vásquez-Tejos ◽  
Hernán Pape-Larre ◽  
Juan Martín Ireta-Sánchez
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Regression Model ◽  
Stock Returns ◽  
Multiple Regression ◽  
Risk Measures ◽  
Liquidity Risk ◽  
Multiple Regression Model ◽  
Developed Markets ◽  
The Impact

This study analyzes the impact of liquidity risk on the return of shares in the Chilean stock market, during the period from January 2000 to July 2018. A large number of studies have focused on measuring this effect in developed markets and few in emerging markets, especially the Chilean one. To do this, we used 6 risk measures in a multiple regression model; four widely used in previous studies and two new proposed measures. We found evidence of the significance of the liquidity risk over the stock return.RESUMENEste estudio analiza el impacto del riesgo de liquidez sobre el retorno de las acciones en el mercado bursátil chileno, durante el periodo de enero de 2000 hasta julio de 2018. Gran cantidad de estudios se han centrado en medir este efecto en los mercados desarrollados y pocos en mercados emergentes, especialmente el chileno. Para ello, se utilizó un modelo de regresión múltiple 6 medidas de riesgo; cuatro utilizadas ampliamente en estudios anteriores y dos medidas nuevas propuestas. Encontramos evidencia de significancia del riesgo de liquidez sobre el retorno accionario.RESUMOEste estudo analisa o impacto do risco de liquidez no retorno das ações no mercado de ações chileno, durante o período de janeiro de 2000 a julho de 2018. Muitos estudos têm se concentrado em medir este efeito em mercados desenvolvidos e poucos nos mercados emergentes, especialmente o chileno. Para isso, utilizamos 6 medidas de risco em um modelo de regressão múltipla; quatro amplamente utilizados em estudos anteriores e duas novas medidas propostas. Encontramos evidências da significância do risco de liquidez sobre o retorno das ações.  

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.

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.

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