The analysis of the predictive power of the credit spread for future economic activity

2020 ◽  
Vol 18 (1) ◽  
pp. 3-31
Author(s):  
Dong-jun Shin ◽  
Euihwan Park
2012 ◽  
Vol 102 (4) ◽  
pp. 1692-1720 ◽  
Author(s):  
Simon Gilchrist ◽  
Egon Zakrajšek

Using micro-level data, we construct a credit spread index with considerable predictive power for future economic activity. We decompose the credit spread into a component that captures firm-specific information on expected defaults and a residual component–– the excess bond premium. Shocks to the excess bond premium that are orthogonal to the current state of the economy lead to declines in economic activity and asset prices. An increase in the excess bond premium appears to reflect a reduction in the risk-bearing capacity of the financial sector, which induces a contraction in the supply of credit and a deterioration in macroeconomic conditions.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Veli Yilanci ◽  
Onder Ozgur ◽  
Muhammed Sehid Gorus

AbstractThis study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development (OECD) by employing monthly data over the period 1981:1–2018:3. For this purpose, the study uses Granger causality in the frequency domain in the panel setting by decomposing the symmetric and asymmetric fluctuations. This methodology determines whether the predictive power of interested variables is concentrated on quickly, moderately, or slowly fluctuating components. Our findings show that the stock prices have predictive power for future long-term economic activity in the panel setting. However, economic activity has more reliable information for stock prices for negative components. Additionally, empirical findings for asymmetric shocks are not fully consistent with those of symmetric ones. Besides, the country-specific results provide different causal linkages across members and frequencies. These findings may provide valuable information for policymakers to design proper and effective policies in OECD countries regarding the stock market and economic activity nexus.


2020 ◽  
Vol 13 (2) ◽  
pp. 20 ◽  
Author(s):  
Hai Lin ◽  
Xinyuan Tao ◽  
Junbo Wang ◽  
Chunchi Wu

Using an aggregate credit spread index, we find that it has substantial predictive power for corporate bond returns over short and long horizons. The return predictability is economically and statistically significant and robust to various controls. The credit spread index and its components have more predictive power for bond returns than conventional default and term spreads. When decomposing the credit spread index into investment- and speculative-grade components, the latter has more predictive power for future bond returns. The source of the index’s predictive power is from its ability to forecast future economic conditions.


2014 ◽  
Vol 12 (1) ◽  
pp. 325-329 ◽  
Author(s):  
Andre Carvalhal ◽  
Miguel Murillo

This paper uses a forecasting model for real economic activity for a group of emerging economies (Brazil, India, Mexico and Russia) based on the information contained in their capital markets. We forecast the industrial production in emerging markets throughout different time horizons using information contained in stock and fixed-income markets. Our results suggest that fixed-income and stock markets do not reveal information regarding future economic growth in Brazil, Mexico and Russia. In the case of India, the yield spread explain part of the variation of the economic activity, but the stock market does not have predictive power.


2020 ◽  
Vol 79 (4) ◽  
pp. 75-97
Author(s):  
Filipp Ulyankin ◽  

Modern economic literature features quite a number of various indices of economic activity. Some of them are based on public opinion polls (‘manual’ indices), while others are based on unstructured data from the Internet (‘automatic’ indices). However, the question as to which of these approaches is the most effective remains open. In this paper, we compare several different indices of economic activity in terms of their explanatory and predictive power. We build ‘automatic’ indices using machine learning methods. Search queries, news articles and user comments under news posts from social media are used as source data. The analysis of the resulting indices of economic activity shows that the search and news indices Granger-cause ‘manual’ indices and also better explain and predict the set of macroeconomic variables selected for research. The good explanatory power of the current values of macroeconomic indicators by means of current indices of economic activity with a lag in the output of macroeconomic statistics makes them suitable for nowcasting.


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