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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.

Long-range dependence in DataCenter networks transmission

Paszkiewicz A., Bolanowski M.

Measurement Automation Monitoring

2017

Vol. 63, No. 8

275--277

The paper presents the mechanisms of long-range dependence measurement in the context of data transmission in Data Center networks. The research involved mainly analyzing network traffic generated by protocols such as CIFS and iSCSI, which are commonly used in such infrastructures. The purpose of the paper was to determine whether the network traffic of above mentioned protocols encapsulated in TCP/IP protocol will have persistent, anti-persist, or random walk character. By indicating long-range dependencies for this type of network traffic, it will be possible to develop effective mechanisms for detecting anomaly in its transmission as well as flow control, including QoS mechanisms, load balancing, etc.