Evaluation and predictability of performance Sector portfolio’s and Al-Quds Index / Evidence from Palestine Exchange

2017 ◽  
Vol 5 (1) ◽  
pp. 16-34
Author(s):  
Mohamad Kamal Abu Amsha
Keyword(s):  
Financial Services ◽  
Autoregressive Model ◽  
Stock Exchange ◽  
Seasonal Pattern ◽  
Model Fitting ◽  
Financial Services Industry ◽  
Market Portfolio ◽  
Services Industry

This paper aimed to evaluation and prediction the portfolios sectors (banking and financial services, industry, insurance, investment, services, market portfolio) in Palestine Stock Exchange (PSE) from January 2013 - December 2016, we had been used, Sharpe index, Treynor, and Jensen index, to evaluate the performance of the portfolios sectors, and Model was used, (Box - Jenkins), to Prediction the performance of the portfolios sectors. The main results of this study to outweigh the performance of the banking portfolio, the performance of other portfolios. The study also found, according to different methods and tests, the autoregressive model of degree one is the best model fitting the data. The seasonal pattern of the series has little effects on the model.

Spectral analysis of heart rate, arterial pressure, and muscle sympathetic nerve activity in normal humans

1998 ◽  
Vol 274 (4) ◽  
pp. H1211-H1217 ◽  
Author(s):  
Akio Nakata ◽  
Shigeo Takata ◽  
Toyoshi Yuasa ◽  
Atsuhiro Shimakura ◽  
Michiro Maruyama ◽  
...  
Keyword(s):  
Heart Rate ◽  
Spectral Analysis ◽  
Arterial Pressure ◽  
Sympathetic Nerve ◽  
Autoregressive Model ◽  
Sympathetic Nerve Activity ◽  
Muscle Sympathetic Nerve Activity ◽  
Model Fitting ◽  
Power Spectral Analysis ◽  
Nerve Activity

We investigated the frequency components of fluctuations in heart rate, arterial pressure, respiration, and muscle sympathetic nerve activity (MSNA) in 11 healthy women using an autoregressive model and examined the relation among variables using Akaike’s relative power contribution analysis with multivariate autoregressive model fitting. Power spectral analysis of MSNA revealed two peaks, with low-frequency (LF) and high-frequency (HF) components. The LF component of MSNA was a major determinant of the LF component of arterial pressure and R-R interval variability (0.70 ± 0.07 and 0.18 ± 0.05, respectively). The effect of the LF component of MSNA on arterial pressure showed no change in response to propranolol but was diminished (0.35 ± 0.08) by phentolamine ( P < 0.02). The effect of the LF component of MSNA on R-R interval was not altered by pharmacological sympathetic nerve blockade. The HF component of MSNA did not influence other variables but was influenced by R-R interval, arterial pressure, and respiration. These findings indicate that the LF component of MSNA reflects autonomic oscillations, whereas the HF component is passive and influenced by other cardiovascular variables.

On a distributional bound arising in autoregressive model fitting

1994 ◽  
Vol 31 (2) ◽  
pp. 401-408 ◽  
Author(s):  
F. Papangelou
Keyword(s):  
Autoregressive Model ◽  
Infinite Order ◽  
Model Fitting ◽  
Uniform Boundedness ◽  
Covariance Matrices ◽  
Large Sample Size ◽  
Ergodic Theorems ◽  
Sample Covariance Matrices ◽  
Sample Covariance ◽  
Special Case

In the theory of autoregressive model fitting it is of interest to know the asymptotic behaviour, for large sample size, of the coefficients fitted. A significant role is played in this connection by the moments of the norms of the inverse sample covariance matrices. We establish uniform boundedness results for these, first under generally weak conditions and then for the special case of (infinite order) processes. These in turn imply corresponding ergodic theorems for the matrices in question.

On a distributional bound arising in autoregressive model fitting

1994 ◽  
Vol 31 (02) ◽  
pp. 401-408
Author(s):  
F. Papangelou
Keyword(s):  
Autoregressive Model ◽  
Infinite Order ◽  
Model Fitting ◽  
Uniform Boundedness ◽  
Covariance Matrices ◽  
Large Sample Size ◽  
Ergodic Theorems ◽  
Sample Covariance Matrices ◽  
Sample Covariance ◽  
Special Case

In the theory of autoregressive model fitting it is of interest to know the asymptotic behaviour, for large sample size, of the coefficients fitted. A significant role is played in this connection by the moments of the norms of the inverse sample covariance matrices. We establish uniform boundedness results for these, first under generally weak conditions and then for the special case of (infinite order) processes. These in turn imply corresponding ergodic theorems for the matrices in question.

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