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Faktorji z vplivom na pričakovane donose evropskih bank : magistrsko delo
ID Sečnik, Taja (Author), ID Košir, Tomaž (Mentor) More about this mentor... This link opens in a new window

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Abstract
Delo se ukvarja z razlago gibanja cen delnic evropskih bank med letoma 2000 in 2018 z uporabo različnih modelov, podatkov in orodij. Na začetku se posvetimo analizi nekaterih najpomembnejših modelov, ki se uporabljajo za razlago donosov finančnih instrumentov. Modele razširimo z dodatnimi faktorji, za katere domnevamo, da imajo vpliv na gibanje cen bančnih delnic in ki so v večini povezani z obrestnimi merami. S primerjavo statističnih kriterijev ugotovimo, kateri faktorji in modeli se obnesejo bolje na danem vzorcu bank. Pri tem naredimo tudi primerjavo med velikimi in manjšimi bankami, med ameriškimi in evropskimi trgi ter med posameznimi evropskimi državami, kjer opazimo pomembne razlike med danimi vzorci bank. Predvsem opazimo razlike med bolj in manj razvitimi delniškimi trgi, saj imajo modeli večjo pojasnjevalno moč na bolj razvitih trgih, pomembnejši pa so faktorji, ki odražajo splošno stanje trga, med tem ko so na manj razvitih trgih pomembnejši faktorji v povezavi z obrestnimi merami. Podobno velja za razliko med velikimi in manjšimi bankami, saj se kot za bolj razvite trge tudi za velike banke kot pomembnejši izkažejo tržni faktorji, za manjše pa imajo modeli slabšo pojasnjevalno moč, h kateri pa močneje prispevajo faktorji v povezavi z obrestnimi merami. V nadaljevanju s pomočjo Bayesovega pristopa k izbiri modela med množico vseh faktorjev določimo najboljši model za posamezno skupino bank. Enak pristop uporabimo tudi na množici podatkov, ki so na voljo s strani bančnega nadzora v Evropi in jih letno objavlja Evropski bančni organ. S pomočjo te metode določimo množico podatkovnih točk s poudarkom na geografskih izpostavljenostih, ki bi lahko vplivale na gibanje cen delnic bank v vzorcu.

Language:Slovenian
Keywords:bančni donosi, sistemsko tveganje, Bayesov pristop k izbiri modela, podatki bančnega nadzora
Work type:Master's thesis/paper
Organization:FMF - Faculty of Mathematics and Physics
Year:2019
PID:20.500.12556/RUL-108173 This link opens in a new window
UDC:519.2
COBISS.SI-ID:18660697 This link opens in a new window
Publication date in RUL:20.06.2019
Views:837
Downloads:184
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Secondary language

Language:English
Title:Factors with effect on expected European bank returns
Abstract:
This paper examines the returns of European bank stocks between years 2000 and 2018 using different models, data and tools. We start with a comparison of the standard market models used for understanding the returns of financial instruments. We expand the models with additional factors, for which we can assume a significant impact on the bank returns, mostly in relation with interest rates. By comparing statistical criteria we determine the factors and models with the best explanatory power for a given set of banks. We also compare the efficiency of models on sets of big and smaller banks, European and U.S. markets, and individual European countries. We note a significant difference between the different subsets of banks, especially between more and less developed markets, as in general, all models have a higher explanatory power when applied on banks operating in more developed stock exchange markets, and factors reflecting the general state of the market tend to be more significant. On the other hand, interest rate factors tend to be relatively more significant in less developed markets. Similarly, market factors tend to be more important for big banks, while for smaller banks, the bank specific factors are more significant, and the models have a lower explanatory power. We continue with an application of the Bayes approach to model selection on the aforementioned set of factors in order to determine the best model for a given set of banks. We use the same approach on the sets of banking supervision data in Europe, made available on an annual basis by the European Banking Authority. Using this method we determine a set of data points and geographical exposures that might have an impact on the returns of European banks.

Keywords:bank returns, systemic risk, Bayes approach to model selection, banking supervision data

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