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The Impact of Active Aggregate Demand on Utilisation-adjusted TFP
Konstantin Gantert
IWH Discussion Papers,
Nr. 9,
2022
Abstract
Non-clearing goods markets are an important driver of capacity utilisation and total factor productivity (TFP). The trade-off between goods prices and household search effort is central to goods market matching and therefore drives TFP over the business cycle. In this paper, I develop a New-Keynesian DSGE model with capital utilisation, worker effort, and expand it with goods market search-and-matching (SaM) to model non-clearing goods markets. I conduct a horse-race between the different capacity utilisation channels using Bayesian estimation and capacity utilisation survey data. Models that include goods market SaM improve the data fit, while the capital utilisation and worker effort channels are rendered less important compared to the literature. It follows that TFP fluctuations increase for demand and goods market mismatch shocks, while they decrease for technology shocks. This pattern increases as goods market frictions increase and as prices become stickier. The paper shows the importance of non-clearing goods markets in explaining the difference between technology and TFP over the business cycle.
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The Effect of Language on Investing: Evidence from Searches in Chinese Versus English
Hui-Ching Chuang, Iftekhar Hasan, Yin-Siang Huang, Chih-Yung Lin
Pacific-Basin Finance Journal,
June
2021
Abstract
This study examines the language effect on investing behavior in local stock markets for local- and foreign-language investors using Google search records. First, we find that attention to a local language stimulates attention to a foreign language, increases abnormal news coverage, and has better predictability on stock returns. Second, investors who do Google searches in the local language react faster to a news event's shock than those who search in the foreign language. Third, only attention to the local language can reduce the price drift of an earnings surprise. Last, firm-level information asymmetry is a channel for local advantage. Therefore, we suggest that investors who use a stock market's local language have a local advantage when seeking more profitable investment opportunities in that stock market.
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What Does Peer-to-Peer Lending Evidence Say About the Risk-taking Channel of Monetary Policy?
Yiping Huang, Xiang Li, Chu Wang
Journal of Corporate Finance,
2021
Abstract
This paper uses loan application-level data from a peer-to-peer lending platform to study the risk-taking channel of monetary policy. By employing a direct ex-ante measure of risk-taking and estimating the simultaneous equations of loan approval and loan amount, we provide evidence of monetary policy's impact on a nonbank financial institution's risk-taking. We find that the search-for-yield is the main driving force of the risk-taking effect, while we do not observe consistent findings of risk-shifting from the liquidity change. Monetary policy easing is associated with a higher probability of granting loans to risky borrowers and greater riskiness of credit allocation. However, these changes do not necessarily relate to a larger loan amount on average.
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What Does Peer-to-Peer Lending Evidence Say About the Risk-taking Channel of Monetary Policy?
Yiping Huang, Xiang Li, Chu Wang
Abstract
This paper uses loan application-level data from a Chinese peer-to-peer lending platform to study the risk-taking channel of monetary policy. By employing a direct ex-ante measure of risk-taking and estimating the simultaneous equations of loan approval and loan amount, we are the first to provide quantitative evidence of the impact of monetary policy on the risk-taking of nonbank financial institution. We find that the search-for-yield is the main workhorse of the risk-taking effect, while we do not observe consistent findings of risk-shifting from the liquidity change. Monetary policy easing is associated with a higher probability of granting loans to risky borrowers and a greater riskiness of credit allocation, but these changes do not necessarily relate to a larger loan amount on average.
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What Does Peer-to-Peer Lending Evidence Say About the Risk-taking Channel of Monetary Policy?
Yiping Huang, Xiang Li, Chu Wang
Abstract
This paper uses loan application-level data from a peer-to-peer lending platform to study the risk-taking channel of monetary policy. By employing a direct ex-ante measure of risk-taking and estimating the simultaneous equations of loan approval and loan amount, we are the first to provide quantitative evidence of the impact of monetary policy on the risk-taking of nonbank financial institution. We find that the search-for-yield is the main workhorse of the risk-taking effect, while we do not observe consistent findings of risk-shifting from the liquidity change. Monetary policy easing is associated with a higher probability of granting loans to risky borrowers and a greater riskiness of credit allocation, but these changes do not necessarily relate to a larger loan amount on average.
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In Search for Yield? Survey-based Evidence on Bank Risk Taking
Claudia M. Buch, S. Eickmeier, Esteban Prieto
Journal of Economic Dynamics and Control,
Nr. 43,
2014
Abstract
Monetary policy can have an impact on economic and financial stability through the risk taking of banks. Falling interest rates might induce investment into risky activities. This paper provides evidence on the link between monetary policy and bank risk taking. We use a factor-augmented vector autoregressive model (FAVAR) for the US for the period 1997–2008. Besides standard macroeconomic indicators, we include factors summarizing information provided in the Federal Reserve’s Survey of Terms of Business Lending (STBL). These data provide information on banks׳ new loans as well as interest rates for different loan risk categories and different banking groups. We identify a risk-taking channel of monetary policy by distinguishing responses to monetary policy shocks across different types of banks and different loan risk categories. Following an expansionary monetary policy shock, small domestic banks increase their exposure to risk. Large domestic banks do not change their risk exposure. Foreign banks take on more risk only in the mid-2000s, when interest rates were ‘too low for too long’.
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