scholarly journals Predictability in Stock Returns in an Emerging Market: Evidence from KSE 100 Stock Price Index

2006 ◽  
Vol 45 (3) ◽  
pp. 369-381 ◽  
Author(s):  
Khurshid M. Kiani
Keyword(s):  
State Space ◽  
Stock Returns ◽  
Stock Price ◽  
Emerging Market ◽  
State Space Model ◽  
Gaussian State ◽  
Policy Action ◽  
Treasury Bills ◽  
Excess Stock Returns ◽  
Non Gaussian

We investigate the persistence in monthly KSE100 excess stock returns over the Treasury bills rates using non-Gaussian state space or unobservable component model with stable distributions and volatility persistence. Results from our non-Gaussian state space model, which is an improvement over Conard and Kaul (1988), show that the conditional distribution has a stable of 1.748 and normality is rejected even after accounting for GARCH. There exists a statistically significant predictable component in the KSE 100 excess stock returns. The optimal predictor in the unconditional expectation of the series is estimated to be 0.18 percent per annum. An evidence of highly nonconstant scales in different periods of time exhibits a tendency towards stock market crashes which invites remedial policy action.

A non-linear and non-Gaussian state-space model for censored air pollution data

2005 ◽  
Vol 16 (2) ◽  
pp. 167-180 ◽  
Author(s):  
Craig J. Johns ◽  
Robert H. Shumway
Keyword(s):  
Air Pollution ◽  
State Space ◽  
State Space Model ◽  
Gaussian State ◽  
Space Model ◽  
Non Linear ◽  
Non Gaussian

PMCMC for Term Structure of Interest Rates under Markov Regime Switching and Jumps

2020 ◽  
Vol 8 (2) ◽  
pp. 159-169
Author(s):  
Xiangdong Liu ◽  
Xianglong Li ◽  
Shaozhi Zheng ◽  
Hangyong Qian
Keyword(s):  
Parameter Estimation ◽  
Interest Rates ◽  
Closed Form ◽  
State Space ◽  
Term Structure ◽  
Regime Switching ◽  
State Space Model ◽  
Gaussian State ◽  
Space Model ◽  
Non Gaussian

AbstractA parameter estimation method, called PMCMC in this paper, is proposed to estimate a continuous-time model of the term structure of interests under Markov regime switching and jumps. There is a closed form solution to term structure of interest rates under Markov regime. However, the model is extended to be a CKLS model with non-closed form solutions which is a typical nonlinear and non-Gaussian state-space model(SSM) in the case of adding jumps. Although the difficulty of parameter estimation greatly prevents from researching models, we prove that the nonlinear and non-Gaussian state-space model has better performances in studying volatility. The method proposed in this paper will be implemented in simulation and empirical study for SHIBOR. Empirical results illustrate that the PMCMC algorithm has powerful advantages in tackling the models.

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