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Estimating mean-standard deviation ratios of financial data
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Economics, Finance and Statistics.
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Economics, Finance and Statistics.
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Economics, Finance and Statistics.
2012 (engelsk)Inngår i: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, nr 3, s. 657-671Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2012. Vol. 39, nr 3, s. 657-671
Emneord [en]
return-risk ratio, increasing dimension asymptotics, coefficient of variation, Arbitrage Pricing Theory model
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-15730DOI: 10.1080/02664763.2011.610443Scopus ID: 2-s2.0-84856881341OAI: oai:DiVA.org:hj-15730DiVA, id: diva2:432170
Tilgjengelig fra: 2011-08-01 Laget: 2011-08-01 Sist oppdatert: 2019-02-21bibliografisk kontrollert
Inngår i avhandling
1. Assessing Distributional Properties of High-Dimensional Data
Åpne denne publikasjonen i ny fane eller vindu >>Assessing Distributional Properties of High-Dimensional Data
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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

sted, utgiver, år, opplag, sider
Jönköping: Jönköping International Business School, 2013. s. 27
Serie
JIBS Dissertation Series, ISSN 1403-0470 ; 092
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-22547 (URN)978-91-86345-46-4 (ISBN)
Disputas
2013-11-29, B1014, Jönköping International Business School, Gjuterigatan 5, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2013-11-07 Laget: 2013-11-07 Sist oppdatert: 2013-11-07bibliografisk kontrollert
2. Issues of incompleteness, outliers and asymptotics in high dimensional data
Åpne denne publikasjonen i ny fane eller vindu >>Issues of incompleteness, outliers and asymptotics in high dimensional data
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This thesis consists of four individual essays and an introduction chapter. The essays are in the field of multivariate statistical analysis of High dimensional data. The first essay presents the issue of estimating the inverse covariance matrix alone and when it is used within the Mahalanobis distance in High-dimensional data. Three types of ridge-shrinkage estimators of the inverse covariance matrix are suggested and evaluated through Monte Carlo simulations. The second essay deals with incomplete observations in empirical applications of the Arbitrage Pricing Theory model and the interest is to model the underlying covariance structure among the variables by a few common factors. Two possible solutions to the problem are considered and a

case study using the Swedish OMX data is conducted for demonstration. In the third essay the issue of outlier detection in High-dimensional data is treated. A number of point estimators of the Mahalanobis distance are suggested and their properties are evaluated. In the fourth and last essay the relation between the second central moment of a distribution to its first raw moment is considered in an financial context. Three possible estimators are considered and it is shown that they are consistent even when the dimension increases proportionally to the number of observations.

sted, utgiver, år, opplag, sider
Jönköping: Jönköping International Business School, 2011. s. 119
Serie
JIBS Dissertation Series, ISSN 1403-0470 ; 069
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-14934 (URN)9789186345181 (ISBN)
Disputas
2011-04-29, B1014, 10:00 (engelsk)
Veileder
Tilgjengelig fra: 2011-05-03 Laget: 2011-05-03 Sist oppdatert: 2016-10-13bibliografisk kontrollert

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