Open AccessFormation of Investment Portfolios of Two Assets Based on Forecast Returns Using the ARFIMA-GARCH Model
Author(s) -
Robert Garafutdinov
Publication year - 2021
Publication title -
vestnik volgogradskogo gosudarstvennogo universiteta. èkonomika
Language(s) - English
Resource type - Journals
eISSN - 2713-1599
pISSN - 2713-1580
DOI - 10.15688/ek.jvolsu.2021.2.11
Subject(s) - autoregressive fractionally integrated moving average , autoregressive conditional heteroskedasticity , heteroscedasticity , econometrics , mathematics , economics , volatility (finance) , long memory
The paper tests the hypothesis that the formation of investment portfolios of two assets based on predicted returns obtained using fractal models with conditional heteroscedasticity (ARFIMA-GARCH) allows to obtain portfolios with better characteristics than those obtained using the ARFIMA model. A computational experiment on artificial data and real data from the Russian stock market was carried out. The software implementation of the hypothesis testing algorithm was carried out using Python and R programming languages. The following results were obtained. Average absolute forecast error of the ARFIMA-GARCH model differs from the ARFIMA model error within the limits of error, statistically significant difference is not revealed (it is true for both model and real data). At the same time, portfolios formed using the GARCH model have, on average, higher returns, and a better return to risk ratio in comparison with portfolios formed using the ARFIMA model. Therefore, the hypothesis about the benefits of fractal GARCH models is not rejected.