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Assessing Distributional Properties of High-Dimensional Data
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Economics, Finance and Statistics.
2013 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

This doctoral thesis consists of five papers in the field of multivariate statistical analysis of high-dimensional data. Because of the wide application and methodological scope, the individual papers in the thesis necessarily target a number of different statistical issues. In the first paper, Monte Carlo simulations are used to investigate a number of tests of multivariate non-normality with respect to their increasing dimension asymptotic (IDA) properties as the dimension p grows proportionally with the number of observations n such that p/n → c where is a constant. In the second paper a new test for non-normality that utilizes principal components is proposed for cases when p/n → c. The power and size of the test are examined through Monte Carlo simulations where different combinations of p and n are used.

The third paper treats the problem of the relation between the second central moment of a distribution to its first raw moment. In order to make inference of the systematic relationship between mean and standard deviation, a model that captures this relationship by a slope parameter (β) is proposed and three different estimators of this parameter are developed and their consistency proven in the context where the number of variables increases proportionally to the number of observations. In the fourth paper, a Bayesian regression approach has been taken to model the relationship between the mean and standard deviation of the excess return and to test hypotheses regarding the β parameter. An empirical example involving Stockholm exchange market data is included. Then finally in the fifth paper three new methods to test for panel cointegration

Ort, förlag, år, upplaga, sidor
Jönköping: Jönköping International Business School , 2013. , s. 27
Serie
JIBS Dissertation Series, ISSN 1403-0470 ; 092
Nationell ämneskategori
Ekonomi och näringsliv
Identifikatorer
URN: urn:nbn:se:hj:diva-22547ISBN: 978-91-86345-46-4 (tryckt)OAI: oai:DiVA.org:hj-22547DiVA, id: diva2:662586
Disputation
2013-11-29, B1014, Jönköping International Business School, Gjuterigatan 5, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2013-11-07 Skapad: 2013-11-07 Senast uppdaterad: 2013-11-07Bibliografiskt granskad
Delarbeten
1. Assessing Normality of High-Dimensional Data
Öppna denna publikation i ny flik eller fönster >>Assessing Normality of High-Dimensional Data
2013 (Engelska)Ingår i: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, nr 2, s. 360-369Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The assumption of normality is crucial in many multivariate inference methods and may be even more important when the dimension of data is proportional to the sample size. It is therefore necessary that tests for multivariate non normality remain well behaved in such settings. In this article, we examine the properties of three common moment-based tests for non normality under increasing dimension asymptotics (IDA). It is demonstrated through Monte Carlo simulations that one of the tests is inconsistent under IDA and that one of them stands out as uniformly superior to the other two.

Nyckelord
Multivariate skewness and kurtosis, Increasing dimension, Asymptotics, Non normality
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
urn:nbn:se:hj:diva-16439 (URN)10.1080/03610918.2011.636164 (DOI)2-s2.0-84870900232 (Scopus ID)
Tillgänglig från: 2011-10-24 Skapad: 2011-10-24 Senast uppdaterad: 2019-02-21Bibliografiskt granskad
2. Using Principal Components to Test Normality of High-Dimensional Data
Öppna denna publikation i ny flik eller fönster >>Using Principal Components to Test Normality of High-Dimensional Data
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Ekonomi och näringsliv
Identifikatorer
urn:nbn:se:hj:diva-22544 (URN)
Tillgänglig från: 2013-11-07 Skapad: 2013-11-07 Senast uppdaterad: 2013-11-07Bibliografiskt granskad
3. Estimating mean-standard deviation ratios of financial data
Öppna denna publikation i ny flik eller fönster >>Estimating mean-standard deviation ratios of financial data
2012 (Engelska)Ingår i: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, nr 3, s. 657-671Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This article treats the problem of linking the relation between excess return and risk of financial assets when the returns follow a factor structure. The authors propose three different estimators and their consistencies are established in cases when the number of assets in the cross-section (n) and the number of observations over time (T) are of comparable size. An empirical investigation is conducted on the Stockholm stock exchange market where the mean-standard deviation ratio is calculated for small- mid- and large cap segments, respectively.

Nyckelord
return-risk ratio, increasing dimension asymptotics, coefficient of variation, Arbitrage Pricing Theory model
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
urn:nbn:se:hj:diva-15730 (URN)10.1080/02664763.2011.610443 (DOI)2-s2.0-84856881341 (Scopus ID)
Tillgänglig från: 2011-08-01 Skapad: 2011-08-01 Senast uppdaterad: 2019-02-21Bibliografiskt granskad
4. A Bayesian Approach for Estimating Mean-Standard Deviation Ratios of Financial Data
Öppna denna publikation i ny flik eller fönster >>A Bayesian Approach for Estimating Mean-Standard Deviation Ratios of Financial Data
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Ekonomi och näringsliv
Identifikatorer
urn:nbn:se:hj:diva-22545 (URN)
Tillgänglig från: 2013-11-07 Skapad: 2013-11-07 Senast uppdaterad: 2013-11-07Bibliografiskt granskad
5. Testing for Panel Cointegration in High-Dimensional Data in the Presence of Cross-Sectional Dependency
Öppna denna publikation i ny flik eller fönster >>Testing for Panel Cointegration in High-Dimensional Data in the Presence of Cross-Sectional Dependency
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Ekonomi och näringsliv
Identifikatorer
urn:nbn:se:hj:diva-22546 (URN)
Tillgänglig från: 2013-11-07 Skapad: 2013-11-07 Senast uppdaterad: 2013-11-07Bibliografiskt granskad

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