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Investment risk assessment based on the long-term memory parameter

Zeug-Żebro Katarzyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2020

z. 144

671--680

Purpose: The presence of a long-term memory component in a time series means that even very distant observations exert a certain influence on subsequent implementations of the process. Generally, this relationship is not particularly strong, but it does exist. Interpreting this phenomenon in the context of financial time series, one can come to the conclusion that information that has affected the market some time ago may still be important for the current quotation. The article is devoted to checking the existence of a long-term memory in the financial time series and assessing the investment risk of these series based on the long-term memory parameter. Design/methodology/approach: In order to study the phenomenon of long-term memory in financial time series, the local Whittle estimator was used, while the investment risk assessment was carried out using the fractal dimension, β-coefficient and standard deviation of rates of return. Findings: In the first part of the study the author indicated time series which were characterized by the phenomenon of long-term memory. Then, on the basis of selected measures, the risk of investment was estimated and shares with the least risk were indicated. Research limitations/implications: The results obtained for selected measures showed discrepancies between the shares with the highest and the lowest level of investment risk. Although the results obtained do not give a definite answer which risk measure is more effective, they encourage the use of other measures related to the phenomenon of long-term memory. Practical implications: Application in portfolio analysis. Originality/value: The use of the long-term memory parameter to assess the investment risk of shares.

General structure of L-lymphocyte applied to event detection in financial time series

Pelech-Pilichowski T., Duda J. T.

Systems Science

2006

Vol. 32, no 4

65-71

The paper is focused on the T-lymphocyte construction applied to immune-inspired event detection in financial time series. The goal is to recognize symptoms of abrupt of long-time mean value of many processed series. The task of the T-lymphocyte is to distinguish between "healthy" and "illness" states through examining individual series, with algorithms based on weak and rigorous statistical tests (detailed operation of detection is shown). General structure of the L-lymphocyte algorithm is illustrated. A comparison of the number of detected symptoms is presented.