The Relationship between Sovereign Credit and EUR/USD Risk Premia in the Eurozone

2014 ◽  
Author(s):  
Matthias Held ◽  
Marcel Omachel
Keyword(s):  
Risk Premia ◽  
Sovereign Credit ◽  
The Relationship

New results on the predictive value of crude oil for US stock returns

2018 ◽  
Vol 35 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Matt Brigida
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
State Space ◽  
Risk Premia ◽  
Time Varying ◽  
Content Type ◽  
Varying Coefficients ◽  
The Us ◽  
The Relationship ◽  
Predictive Relationship

Purpose The purpose of this study is to clarify the nature of the predictive relationship between crude oil and the US stock market, with particular attention to whether this relationship is driven by time-varying risk premia. Design/methodology/approach The authors formulate the predictive regression as a state-space model and estimate the time-varying coefficients via the Kalman filter and prediction-error decomposition. Findings The authors find that the nature of the predictive relationship between crude oil and the US stock market changed in the latter half of 2008. After mid-2008, the predictive relationship switched signs and exhibited characteristics which make it much more likely that the predictive relationship is due to time-varying risk premia rather than a market inefficiency. Originality/value The authors apply a state-space approach to modeling the predictive relationship. This allows one to watch the evolution of the predictive relationship over time. In particular, the authors identify a dramatic shift in the relationship around August 2008. Prior research has not been able to identify shifts in the relationship.

scholarly journals Did the CDS market push up risk premia for sovereign credit?

2011 ◽  
Vol 147 (3) ◽  
pp. 275-302 ◽  
Author(s):  
Sergio Andenmatten ◽  
Felix Brill
Keyword(s):  
Risk Premia ◽  
Sovereign Credit ◽  
Push Up

scholarly journals The relationship between sovereign credit rating and trends of macroeconomic indicators

2019 ◽  
Vol 16 (3) ◽  
pp. 292-306
Author(s):  
Jana Kotěšovcová ◽  
Jiří Mihola ◽  
Petr Budinský
Keyword(s):  
Correlation Coefficient ◽  
Credit Rating ◽  
Financial Assets ◽  
Data Set ◽  
Sovereign Credit ◽  
Macroeconomic Indicators ◽  
Data Source ◽  
The Relationship ◽  
Sovereign Credit Rating

The sovereign credit rating provides information about the creditworthiness of a country and thereby serves as a tool for investors in order to make right decisions concerning financial assets worth investments. Thus, determination of a sovereign credit rating is a highly complex and challenging activity. Specialized agencies are involved in rating assessment. So, it’s essential to analyze the efficiency of their work and seek out easily accessible tools for generating assessments of such ratings. The objective of this article is to find out whether sovereign credit rating can be reliably estimated using trends of selected macroeconomic indicators, despite the fact that sovereign credit rating is most likely influenced by non-economic factors. This can be used for strategic considerations at national and multinational levels. The relationships between sovereign credit rating and the trends of macroeconomic indicators were examined using statistical methods, linear multiple regression analysis, cumulative correlation coefficient, and multicollinearity test. The data source used is comprised of selected World Bank indicators meeting the conditions of completeness and representativeness. The data set has shown a cumulative correlation coefficient value greater than 95%, however at 100% multicollinearity. This is followed by the gradual elimination of indicators, but even this did not allow achieving acceptable values. So, the conclusion is that rating levels are not explainable solely by the trends of economic indicators, but other influences, e.g. political. However, the fact that the statistical model yielded acceptable results for five and fewer indicators allowed a regression equation to be found that gives good estimates of a country’s rating. This allows, for example, predicting of ratings relatively easy by forecasting the development of selected macroeconomic indicators.

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