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• W2600234443 abstract "In this thesis we discuss the asset returns. Our work was initially motivated by Mantegna’s and Stanley’s results (1995) that put forward the stable distribution as a model of asset returns and demonstrated the scaling property that seemed to be present in the data. Nevertheless, that work raised several questions both theoretically interesting and practically challenging such as: what is the effect of measurement quantity on the inference concerning asset returns, which are the proper quantities to look at, does the scaling exists, and if so what are its limits, are there characteristic times on asset returns, how the possible time-dependent variance affects the inference? When exploring these issues, we became concerned about the possible variability and time-dependency of the shape of the asset return distribution in addition to the time-dependent variance. Thus, we speculated that the possible variability of the shape could have been one reason behind the contradictory results concerning the best fitting model of asset returns. Furthermore, the anomalies related to the mean returns and standard deviations led us to raise the question whether the shape of the asset return distribution shows similar kinds of anomalies. Finally, since we noticed that much debate has been had about the time-independent and time-dependent models but there has been relative few studies where these various models have been compared using the same datasets, especially high frequency data, this has been done is thesis and quite surprising results were obtained. In order to address these questions we studied Standard & Poor's 500 daily index data of the New York Stock Exchange from more than 32 years. In addition, we used a high frequency data recorded on about 20 seconds timeinterval over three years time period. For comparison reasons we also studied a small market, namely the Helsinki Stock Exchange all shares return index (HEX) over seven year period. Moreover, we used an artificial data to demonstrate some effects of measurement quantities. Our results show that the proper variable to look at is the logarithmic return. Initially, for short time horizon or holding periods, the truncated Levy distribution was found to fit the data quite well. Since this is not a stable distribution, the scaling behaviour observed for short times should break down for longer times. Thus, we demonstrated that the characteristic time of the breakdown of scaling is of the order of few days. Furthermore, the analysis of convergence of the kurtosis showed that it takes place within few months." @default.
• W2600234443 created "2017-04-07" @default.
• W2600234443 creator A5017309210 @default.
• W2600234443 date "2002-12-20" @default.
• W2600234443 modified "2023-09-25" @default.
• W2600234443 title "Essays on asset return distributions" @default.
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• W2600234443 hasPublicationYear "2002" @default.
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