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EN
Aim/purpose – The purpose of this paper is to examine the influence of internal and external historical determinants on the volatility of banks’ stock returns in the euro zone. A dedicated database has been created to identify factors significantly affecting volatility. Design/methodology/approach – The study is based on information about banks listed on the stock exchanges of the euro zone economies. Quarterly data from the period of 2004-2015 along with static panel models were used as the research method. Findings – Results confirm that selected factors have a significant impact on the analysed variables: the ratio of long-term investments to assets, solvency ratio, price to book value, the unemployment rate, beta, as well as implied volatilities in S&P500 and EUROSTOXX50 indexes. Research implications/limitations – Results can be used to estimate future stock return volatility more accurately. The survey focuses solely on the banking sector, which is the biggest limitations of this research and the findings cannot be used to other sectors. Originality/value/contribution – Most volatility research serves the purpose of predicting future stock prices. Very few papers explain which factors in particular impact volatility.
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EN
The aim of this publication was to analyze the impact of the countires’ credit ratings at the cost of capital. The article was carried out the analysis and review of the existing world literature on the above subject. Authors used a static panel data models for the 135 countries in the period of 2002–2012. As the independent variables were the long- and short-term credit ratings awarded by rating agencies: Standard & Poor’s and Moody’s Investor Service. Credit ratings was converted linearly to the numeric variables. The publication examines how credit ratings and history of credit events affect: the interest rate and maturity of new external debt obligations (public and private), interest rates on deposits and loans, the spreads of interest rates, real interest rates and credit risk premium.
PL
Celem niniejszej publikacji była analiza wpływu ratingów kredytowych krajów na koszt kapitału. W artykule została przeprowadzona analiza i przegląd istniejącej literatury światowej na powyższy temat. Autorzy zastosowali statyczne modele panelowe dla 135 krajów w latach 2002–2012. Jako zmienne niezależne wykorzystano długo- i krótkoterminowe ratingi kredytowe przyznawane przez agencje ratingowe: Standard & Poor’s i Moody’s Investor Service. Ratingi kredytowe skonwertowano liniowo na zmienne numeryczne. W publikacji przeanalizowano jak ratingi kredytowe i historia zdarzeń kredytowych wpływa na: oprocentowanie i zapadalność nowych zewnętrznych zobowiązań dłużnych (publicznych i prywatnych), oprocentowanie lokat i kredytów, spready stóp procentowych, realne stopy procentowe i premię za ryzyko kredytowe.
PL
Celem artykułu jest określenie wpływu determinant na rating kredytowy przyznawany przez agencje ratingowe krajom europejskim. W artykule dokonano przeglądu literaturowego w tym zakresie. Przeanalizowano metodologię nadawania ocen przez poszczególne agencje, a następnie zestawiono otrzymane wyniki. Do badania empirycznego zastosowano statyczne modele panelowe. Wykorzystano dane pochodzące z bazy Banku Światowego oraz z bazy danych Thomson Reuters. Badanie obejmowało lata 2002–2012. Jako zmienne zależne wykorzystano długo- i krótkookresowe ratingi kredytowe przyznawane przez agencje Standard & Poor’s oraz Moody’s Investors Services. Ratingi zdekomponowano liniowo na zmienne numeryczne. Jako zmienne zależne wykorzystano dane makroekonomiczne, m.in. PKB per capita, tempo wzrostu realnego PKB, stopę inflacji, deficyt budżetowy, salda na rachunku obrotów bieżących, zadłużenie zagraniczne do PKB, rezerwy walutowe.
EN
The purpose of this article is to analyze factors that can influence the credit rating granted to European countries by rating agencies. The article analyzes the literature in this field. Methodology used for credit ratings assessment by individual agencies was analyzed, and then the results were summarized. For the empirical study static panel models were used. Data from the World Bank database and the Thomson Reuters database was taken into consideration. The study covered the period 2002–2012. Dependent variables were used as long- and short-term credit ratings given by Standard & Poor's and Moody's Investor Services. The ratings were decomposed linearly for numeric variables. Macroeconomic data was used as dependent variables, among other things, data such as GDP per capita, real GDP growth, inflation, fiscal deficit, balance on the current account, external debt to GDP, foreign exchange reserves.
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