Partial non-Gaussian state space

Biometrika ◽  
1994 ◽  
Vol 81 (1) ◽  
pp. 115-131 ◽  
Author(s):  
N. SHEPHARD
Keyword(s):  
State Space ◽  
Gaussian State ◽  
Non Gaussian

scholarly journals DETECTION LIMITS FOR LINEAR NON-GAUSSIAN STATE-SPACE MODELS

2006 ◽  
Vol 39 (13) ◽  
pp. 282-287 ◽  
Author(s):  
Gustaf Hendeby ◽  
Fredrik Gustafsson
Keyword(s):  
State Space ◽  
Detection Limits ◽  
State Space Models ◽  
Gaussian State ◽  
Non Gaussian

scholarly journals Predicting crypto-currencies using sparse non-Gaussian state space models

2018 ◽  
Vol 37 (6) ◽  
pp. 627-640 ◽  
Author(s):  
Christian Hotz-Behofsits ◽  
Florian Huber ◽  
Thomas Otto Zörner
Keyword(s):  
State Space ◽  
State Space Models ◽  
Gaussian State ◽  
Non Gaussian

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.

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