scholarly journals PEMODELAN DISTRIBUSI TINGKAT IMBAL HASIL INDEKS HARGA DI TUJUH BURSA ASIA

2017 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Brady Rikumahu
Keyword(s):  
Normal Distribution ◽  
Distribution Curve ◽  
Financial Time Series ◽  
Laplace Distribution ◽  
Standard Theory ◽  
Financial Time ◽  
Normal Distribution Curve ◽  
Specific Distribution ◽  
Return Distributions ◽  
Using Data

This paper examines the return distributions of 7 markets in the Asian region, namely Hongkong, Indonesia, Malaysia, Korea, Japan, Shanghai, and Singapore, to find out whether the return distributions in those markets follow a specific distribution. Using data from January 2000 to September 2009, the return distributions of each market were constructed and was first fitted to the normal distribution to find out whether or not each market behaves according to the standard theory of finance and investment – which stated that the financial time series follow a random walk – and thus would fit the normal distribution. The result of fitting the return distributions of the 7 markets to normal distribution shows that none of the return distributions follows the normal distribution as evident from the leptokurtic phenomena marked by the excess kurtosis compared to the normal distribution curve and also from the fatter than normal distribution tails and the existence of returns that lie outside the area predictedby the normal distribution.The return distributions were then fitted to a series of theoretical probability distribution. Each of the distribution was fitted to the theoretical. The results are: the Hongkong and Shanghai markets follow the Laplace distribution while the other five markets: Indonesia, Malaysia, Korea, Japan, and Singapore follow the Johnson SU distribution.

MODELING NONSTATIONARY AND LEPTOKURTIC FINANCIAL TIME SERIES

2014 ◽  
Vol 31 (4) ◽  
pp. 703-728 ◽  
Author(s):  
Ying Chen ◽  
Vladimir Spokoiny
Keyword(s):  
Time Series ◽  
Normal Distribution ◽  
Heavy Tails ◽  
Financial Time Series ◽  
Exponential Smoothing ◽  
Numerical Results ◽  
Financial Time ◽  
Locally Adaptive ◽  
Fully Automatic ◽  
Structural Shifts

Financial time series is often assumed to be stationary and has a normal distribution in the literature. Both assumptions are however unrealistic. This paper proposes a new methodology with a focus on volatility estimation that is able to account for nonstationarity and heavy tails simultaneously. In particular, a local exponential smoothing (LES) approach is developed, in which weak estimates with different memory parameters are aggregated in a locally adaptive way. The procedure is fully automatic and the parameters are tuned by a new propagation approach. The extensive and practically oriented numerical results confirm the desired properties of the constructed estimate: it performs stable in a nearly time homogeneous situation and is sensitive to structural shifts. Our main theoretical “oracle” result claims that the aggregated estimate performs as good as the best estimate in the considered family. The results are stated under realistic and unrestrictive assumptions on the model.

Volatility Modeling with Leverage Effect under Laplace Errors

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhengjun Jiang ◽  
Weixuan Xia
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Financial Time Series ◽  
Laplace Distribution ◽  
Leverage Effect ◽  
Financial Returns ◽  
Financial Time ◽  
Error Distributions ◽  
Time Series Comparison ◽  
Unconditional Variance

AbstractThis paper discusses four GARCH-type models (A-GARCH, NA-GARCH, GJR-GARCH, and E-GARCH) in representing volatility of financial returns with leverage effect. In these models, errors are assumed to follow a Laplace distribution in order to deal with the typical leptokurtic feature of financial returns. The properties of these models are analyzed theoretically in terms of unconditional variance, kurtosis, autocorrelation function and news impact, and are further examined in the applications to real financial time series. Comparison is made with other choices of error distributions such as normal, Student-5, and Student-7 distributions, respectively. We also conduct residual analyses to justify the choice of error distributions and find that Laplace-E-GARCH model still performs very well. Our main purpose is to study and compare the proposed models’ relative adequacies and underlying limitations.

scholarly journals POST EXAM ANALYSIS SELECTION OF HIGH AND LOW EXAM RESULT GROUPS BY NORMAL DISTRIBUTION CURVE

2021 ◽  
Vol 13 (1) ◽  
pp. 39-46
Author(s):  
Faiz Marikar
Keyword(s):  
Normal Distribution ◽  
Standard Error ◽  
Distribution Curve ◽  
Standard Normal Distribution ◽  
Constructive Feedback ◽  
Normal Distribution Curve ◽  
Significant Difference ◽  
Difficulty Index ◽  
Standard Error Of Measurement ◽  
Error Of Measurement

The key factor of an assessment is to minimize the errors by having a good reliability and validity of the assessment yardstick. To achieve high score in the test examinee must be aware about assessment cycle and use it in appropriate way in post exam analysis. Outcome of the results can be utilized as a constructive feedback in any given program. This cross-sectional study was conducted at department of Biochemistry, University of Rajarata. Multiple choice questions, structured essay type questions, objective structured practical examination, and continuous assessment was used in this study. Total number of students are 180 and was assessed for difficulty index, discrimination index, reliability, and standard error of measurement. In this study sample for analysis was used basically the examiner divides students into two groups (‘high’ and ‘low’) according to the score sheet of each student. Most of them are doing in a wrong way basically they divide high and low clusters as 25% each and considered upper quartile and lower quartile. In this study we compared it with the standard normal distribution curve where high and low groups are considered as 16% where is the standard. There is no significant difference among both clusters, and we recommend using the standard 16% as the high and low groups in post examination analysis. Keywords: difficulty index, post examination analysis, reliability of the examination, standard error of measurement

scholarly journals AUTOMATIC DETECTION AND RECOGNITION OF CRATERS BASED ON THE SPECTRAL FEATURES OF LUNAR ROCKS AND MINERALS

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 199-204
Author(s):  
L. Ye ◽  
X. Xu ◽  
D. Luan ◽  
W. Jiang ◽  
Z. Kang
Keyword(s):  
Normal Distribution ◽  
Distribution Curve ◽  
Lunar Regolith ◽  
Spectral Characteristics ◽  
Impact Craters ◽  
Simple Type ◽  
Curve Type ◽  
Normal Distribution Curve ◽  
Profile Fitting ◽  
The Impact

Crater-detection approaches can be divided into four categories: manual recognition, shape-profile fitting algorithms, machine-learning methods and geological information-based analysis using terrain and spectral data. The mainstream method is Shape-profile fitting algorithms. Many scholars throughout the world use the illumination gradient information to fit standard circles by least square method. Although this method has achieved good results, it is difficult to identify the craters with poor "visibility", complex structure and composition. Moreover, the accuracy of recognition is difficult to be improved due to the multiple solutions and noise interference. Aiming at the problem, we propose a method for the automatic extraction of impact craters based on spectral characteristics of the moon rocks and minerals: 1) Under the condition of sunlight, the impact craters are extracted from MI by condition matching and the positions as well as diameters of the craters are obtained. 2) Regolith is spilled while lunar is impacted and one of the elements of lunar regolith is iron. Therefore, incorrectly extracted impact craters can be removed by judging whether the crater contains "non iron" element. 3) Craters which are extracted correctly, are divided into two types: simple type and complex type according to their diameters. 4) Get the information of titanium and match the titanium distribution of the complex craters with normal distribution curve, then calculate the goodness of fit and set the threshold. The complex craters can be divided into two types: normal distribution curve type of titanium and non normal distribution curve type of titanium. We validated our proposed method with MI acquired by SELENE. Experimental results demonstrate that the proposed method has good performance in the test area.

scholarly journals Probability Density Functions for Prediction Using Normal and Exponential Distribution

2021 ◽  
pp. 40-52
Author(s):  
Kunio Takezawa
Keyword(s):  
Probability Density Function ◽  
Normal Distribution ◽  
Probability Density ◽  
Density Function ◽  
Exponential Distribution ◽  
Predictive Ability ◽  
Probability Density Functions ◽  
Density Functions ◽  
Specific Distribution ◽  
Using Data

When data are found to be realizations of a specific distribution, constructing the probability density function based on this distribution may not lead to the best prediction result. In this study, numerical simulations are conducted using data that follow a normal distribution, and we examine whether probability density functions that have shapes different from that of the normal distribution can yield larger log-likelihoods than the normal distribution in the light of future data. The results indicate that fitting realizations of the normal distribution to a different probability density function produces better results from the perspective of predictive ability. Similarly, a set of simulations using the exponential distribution shows that better predictions are obtained when the corresponding realizations are fitted to a probability density function that is slightly different from the exponential distribution. These observations demonstrate that when the form of the probability density function that generates the data is known, the use of another form of the probability density function may achieve more desirable results from the standpoint of prediction.

Differential Response of Corn Hybrids and Inbreds to Metolachlor

Weed Science ◽  
1990 ◽  
Vol 38 (6) ◽  
pp. 563-566 ◽  
Author(s):  
Loston Rowe ◽  
Elmer Rossman ◽  
Donald Penner
Keyword(s):  
Great Lakes ◽  
Normal Distribution ◽  
Distribution Curve ◽  
Differential Response ◽  
The Other ◽  
Laboratory Studies ◽  
Corn Hybrids ◽  
Normal Distribution Curve ◽  
Site Of Action

Greenhouse studies were conducted to determine the response of 200 corn hybrids and 29 inbreds to metolachlor applied at 4.5 kg ai ha−1. Both hybrids and inbreds varied in their response to the herbicide. The distribution of injury revealed a normal distribution curve with most of the hybrids having a midlevel of tolerance. Some hybrids were very tolerant, while others were quite sensitive. Laboratory studies were conducted to evaluate absorption and metabolism of14C-metolachlor for a subset of tolerant and sensitive hybrids. There was no observed difference in the product of metolachlor metabolism in the tolerant and sensitive hybrids. The observed variability in metolachlor tolerance among hybrids appeared due to differences in the amount of metolachlor absorption and metabolism and differences at the site of metolachlor action. The tolerant ‘Great Lakes 584’ hybrid absorbed significantly less14C-metolachlor than did the sensitive ‘Pioneer 3744’, while the tolerant ‘Cargill 7567’ metabolized significantly faster more14C-metolachlor than the other hybrids. The internal concentrations of available14C-metolachlor were the same for the tolerant Cargill 7567 and the sensitive ‘Northrup King 9283’ after 8 h, indicating differences at the site of action of metolachlor for these two hybrids.

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