Time-varying predictive content of financial variables in forecasting GDP growth in the G-7 countries

2019 ◽  
Vol 71 ◽  
pp. 211-222 ◽  
Author(s):  
Petri Kuosmanen ◽  
Juuso Vataja
2020 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Julien Chevallier

In the Dynamic Conditional Correlation with Mixed Data Sampling (DCC-MIDAS) framework, we scrutinize the correlations between the macro-financial environment and CO2 emissions in the aftermath of the COVID-19 diffusion. The main original idea is that the economy’s lock-down will alleviate part of the greenhouse gases’ burden that human activity induces on the environment. We capture the time-varying correlations between U.S. COVID-19 confirmed cases, deaths, and recovered cases that were recorded by the Johns Hopkins Coronavirus Center, on the one hand; U.S. Total Industrial Production Index and Total Fossil Fuels CO2 emissions from the U.S. Energy Information Administration on the other hand. High-frequency data for U.S. stock markets are included with five-minute realized volatility from the Oxford-Man Institute of Quantitative Finance. The DCC-MIDAS approach indicates that COVID-19 confirmed cases and deaths negatively influence the macro-financial variables and CO2 emissions. We quantify the time-varying correlations of CO2 emissions with either COVID-19 confirmed cases or COVID-19 deaths to sharply decrease by −15% to −30%. The main takeaway is that we track correlations and reveal a recessionary outlook against the background of the pandemic.


2019 ◽  
Vol 2 (2) ◽  
pp. 258-276
Author(s):  
Nan Li ◽  
Liu Yuanchun

Purpose The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations. Design/methodology/approach The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation. Findings Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI. Originality/value FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.


2017 ◽  
Vol 50 (3) ◽  
pp. 259-277 ◽  
Author(s):  
Petri Kuosmanen ◽  
Juuso Vataja

2013 ◽  
Vol 89 (2) ◽  
pp. 669-694 ◽  
Author(s):  
Yaniv Konchitchki ◽  
Panos N. Patatoukas

ABSTRACT In this study, we hypothesize and find that financial statement analysis of firm profitability drivers applied at the aggregate level yields timely insights that are relevant for forecasting real economic activity. We first show that focusing on the 100 largest firms offers a cost-effective way to extract information embedded in accounting profitability data of the entire stock market portfolio. We then show that accounting profitability data aggregated across the 100 largest firms have predictive content for subsequent real Gross Domestic Product (GDP) growth. We also show that stock market returns have predictive content for future real GDP growth, while their predictive power varies with the length of the measurement window with annual stock market returns being the most powerful. Importantly, we find that the predictive content of our indices of aggregate accounting profitability drivers is incremental to that of annual stock market returns. An in-depth investigation of consensus survey forecasts shows that professional macro forecasters revise their expectations of real economic activity in the direction of the predictive content of aggregate accounting profitability drivers and stock market returns. Although macro forecasters are fully attuned to stock market return data, their forecasts of real GDP growth can be improved in a statistically and economically significant way using our indices of aggregate accounting profitability drivers. Our findings suggest that professional macro forecasters and stock market investors do not fully impound the predictive content of aggregate accounting profitability drivers when forecasting real economic activity. In additional analysis, we examine the association between stock market returns and the portion of subsequent real GDP growth that is predictable based on our indices of aggregate accounting profitability drivers but that is not anticipated by stock market investors. We find that this portion is positively related to stock market returns, suggesting that the macro predictive content of aggregate accounting profitability drivers is relevant for stock valuation. Overall, our study brings financial statement analysis to the forefront as an incrementally useful tool for gauging the prospects of the real economy that should be of interest to academics and practitioners. JEL Classification: E01; E32; E60; M41. Data Availability: Data are available from public sources indicated in the text.


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