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Wyszukujesz frazę "MGARCH model" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Has the pandemic changed the relationships between fintechs and banks?
Autorzy:
Będowska-Sójka, Barbara
Kliber, Agata
Laidroo, Laivi
Powiązania:
https://bibliotekanauki.pl/articles/27315315.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
fintech
banks
COVID-19
cumulative return
MGARCH model
quantile coherency
Opis:
We examine the impact of COVID-19 on the banking and fintech sectors based on the relationships of the respective stock indices from December 2017 to April 2022. We analyse dynamic correlations within multivariate GARCH models and relationships in tails with the quantile coherency approach. Returns of fintech and banks dropped simultaneously at the beginning of the pandemic, but the analysis of cumulative returns and draw-downs reveals that the former recovered faster. Banks and fintechs experienced sharp declines together and fintech experienced extreme growth during the downfalls in the banking sector. However, the latter relationship disappears when we analyze only the banks from the USA and Eurozone. Thus, integrating with fintech may be especially beneficial for banks outside those regions. The ability of fintech to resurface and continue to grow demonstrates its importance in the financial system and confirms the shift toward a digital economy in financial markets.
Źródło:
Operations Research and Decisions; 2023, 33, 4; 15--33
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Long-Run Relationship between Daily Prices on Two Markets: The Bayesian VAR(2)–MSF-SBEKK Model
Autorzy:
Osiewalski, Krzysztof
Osiewalski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/483271.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Bayesian econometrics
vector error correction model
hybrid MGARCH-MSV processes
financial markets
commodity markets
Opis:
We develop a fully Bayesian framework for analysis and comparison of two competing approaches to modelling daily prices on different markets. The first approach, prevailing in financial econometrics, amounts to assuming that logarithms of prices behave like a multivariate random walk; this approach describes logarithmic returns most often by the VAR(1) model with MGARCH (or sometimes MSV) disturbances. In the second approach, considered here, it is assumed that daily price levels are linked together and, thus, the error correction term is added to the usual VAR(1)–MGARCH or VAR(1)–MSV model for logarithmic returns, leading to a reduced rank VAR(2) specification for logarithms of prices. The model proposed in the paper uses a hybrid MSVMGARCH structure for VAR(2) disturbances. In order to keep cointegration modelling as simple as possible, we restrict to the case of two prices representing two different markets. The aim of the paper is to show how to check if a long-run relationship between daily prices exists and whether taking it into account influences our inference on volatility and short-run relations between returns on different markets. In the empirical example the daily values of the S&P500 index and the WTI oil price in the period 19.12.2005 – 30.09.2011 are jointly modelled. It is shown that, although the logarithms of the values of S&P500 and WTI oil price seem to be cointegrated, neglecting the error correction term leads to practically the same conclusions on volatility and conditional correlation as keeping it in the model.
Źródło:
Central European Journal of Economic Modelling and Econometrics; 2013, 5, 1; 65-83
2080-0886
2080-119X
Pojawia się w:
Central European Journal of Economic Modelling and Econometrics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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