Helmut Gründl, Danjela Guxha, A. Kartasheva, Hato Schmeiser
The paper analyzes the scope for the private market for pandemic insurance and discusses the potential role of the financial market and the government. Building on a premise that pandemics are classified as catastrophic risks by the insurance industry, we start by providing a framework that explains theoretically how the catastrophe insurance supply and demand depend on the skewed and fat-tailed loss distributions and the co-movement between insurance stocks performance and the financial market. We use the model to estimate the supply of insurance for natural catastrophes. Then, by using the high-frequency data that tracks the economic impact of the COVID-19 pandemic in the US, we calibrate the loss distribution of a hypothetical insurance contract designed to alleviate the impact of the pandemic on small businesses and employment. The model of catastrophic insurance supply provides a calibration of the supply of pandemic insurance and allows us to compare it to other types of catastrophic insurance. Building on our estimation results, we discuss the scope for the risk transfer to the financial market and the role of the government.
{"title":"Insurability of Pandemic Risks","authors":"Helmut Gründl, Danjela Guxha, A. Kartasheva, Hato Schmeiser","doi":"10.2139/ssrn.3748753","DOIUrl":"https://doi.org/10.2139/ssrn.3748753","url":null,"abstract":"The paper analyzes the scope for the private market for pandemic insurance and discusses the potential role of the financial market and the government. Building on a premise that pandemics are classified as catastrophic risks by the insurance industry, we start by providing a framework that explains theoretically how the catastrophe insurance supply and demand depend on the skewed and fat-tailed loss distributions and the co-movement between insurance stocks performance and the financial market. We use the model to estimate the supply of insurance for natural catastrophes. Then, by using the high-frequency data that tracks the economic impact of the COVID-19 pandemic in the US, we calibrate the loss distribution of a hypothetical insurance contract designed to alleviate the impact of the pandemic on small businesses and employment. The model of catastrophic insurance supply provides a calibration of the supply of pandemic insurance and allows us to compare it to other types of catastrophic insurance. Building on our estimation results, we discuss the scope for the risk transfer to the financial market and the role of the government.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83628415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I investigate how covenants, intrinsic to Collateralized Loan Obligation (CLO) indentures, provide a mechanism through which idiosyncratic shocks may be amplified, imposing negative externalities on other unrelated firms in CLO portfolios. To this aim, I exploit cross-sectional variation in CLO exposure to the Oil & Gas (O&G) industry, as well as the timing of the O&G bust in 2014 to study how non-O&G firms in CLO portfolios are affected. I find that when CLOs are subject to idiosyncratic shocks that push them closer to their covenant constraints, they fire-sell unrelated loans in the secondary loan market to alleviate these constraints. The ex-post, secondary market spread becomes the effective cost of capital for these innocent bystanders, as the expected rate of return across debt instruments is equalized in market equilibrium. In response, firms make financial and real adjustments. These adjustments are most pronounced for riskier firms held in CLO portfolios, whose loans are marked at market prices, as selling mark-to-market loans can generate greater slack in the covenant constraints. As the sample period for this study is 2012-2017, a relatively benign macroeconomic period, the effects may be significantly larger during times of stress such as Spring of 2020, at the outset of the COVID-19 pandemic.
{"title":"The Externalities of Fire Sales: Evidence from Collateralized Loan Obligations","authors":"Shohini Kundu","doi":"10.2139/ssrn.3735645","DOIUrl":"https://doi.org/10.2139/ssrn.3735645","url":null,"abstract":"I investigate how covenants, intrinsic to Collateralized Loan Obligation (CLO) indentures, provide a mechanism through which idiosyncratic shocks may be amplified, imposing negative externalities on other unrelated firms in CLO portfolios. To this aim, I exploit cross-sectional variation in CLO exposure to the Oil & Gas (O&G) industry, as well as the timing of the O&G bust in 2014 to study how non-O&G firms in CLO portfolios are affected. I find that when CLOs are subject to idiosyncratic shocks that push them closer to their covenant constraints, they fire-sell unrelated loans in the secondary loan market to alleviate these constraints. The ex-post, secondary market spread becomes the effective cost of capital for these innocent bystanders, as the expected rate of return across debt instruments is equalized in market equilibrium. In response, firms make financial and real adjustments. These adjustments are most pronounced for riskier firms held in CLO portfolios, whose loans are marked at market prices, as selling mark-to-market loans can generate greater slack in the covenant constraints. As the sample period for this study is 2012-2017, a relatively benign macroeconomic period, the effects may be significantly larger during times of stress such as Spring of 2020, at the outset of the COVID-19 pandemic.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86283196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This dissertation simplifies and substantially improves equity premium and sector return forecasts by combining two prediction models that perform well during different regime states as in Tsiakas et al. (2020) through Dynamic Model Averaging (DMA) of Raftery et al. (2010). This approach allows for statistically significant and economically valuable equity premium forecasts during both recessions and expansions, without needing to predict the regime state explicitly. Extending this approach to sector returns, the adaptability of DMA to sector-specific dynamics allows for return forecasts that outperform all considered alternatives and provide significant economic value in a modified Risk-to-Reward Timing strategy based on Kirby and Ostdiek (2012) over an equally weighted benchmark portfolio spanning all sectors.
{"title":"Exploring the Cyclical Predictability of Sector-Specific Premia","authors":"Till Sänger","doi":"10.2139/ssrn.3731519","DOIUrl":"https://doi.org/10.2139/ssrn.3731519","url":null,"abstract":"This dissertation simplifies and substantially improves equity premium and sector return forecasts by combining two prediction models that perform well during different regime states as in Tsiakas et al. (2020) through Dynamic Model Averaging (DMA) of Raftery et al. (2010). This approach allows for statistically significant and economically valuable equity premium forecasts during both recessions and expansions, without needing to predict the regime state explicitly. Extending this approach to sector returns, the adaptability of DMA to sector-specific dynamics allows for return forecasts that outperform all considered alternatives and provide significant economic value in a modified Risk-to-Reward Timing strategy based on Kirby and Ostdiek (2012) over an equally weighted benchmark portfolio spanning all sectors.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74847547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prognosis of upcoming crises and the course of actually understanding them is increasingly becoming a major subject of discussions in pursuit of reliable indicators The trade war between the United States and China, along with the COVID-19 pandemic are two events that took place in the Chinese economy with the aforementioned characteristics of the Black swan phenomenon, to which this latest professional analysis is devoted The objective of this research is to examine the response of the Shanghai Stock Exchange Composite (SSEC) index, in addition to its relation with macroeconomic variables contributing towards a possible Black Swan Event We employ an econometric methodology comprising of a unit root test, descriptive statistics, linear regression and correlation analysis for the period 2007-2019 Our results illustarte that the bubble from 2015, which is classified as a Black Swan event by many researchers, has a negative influence on the SSEC index We can further deduce that there were some psychological effects on the Chinese stock market that lead to both, positive and negative trends of SSEC indices The main findings confirmed that the Consumer Price Index, Exchange Rate, Interest Rate, Unemployment, GDP and Trade Balance were significantly elaborative macroeconomic variables, that had a substantial impact on the SSEC index
{"title":"Black Swan Event: An Evidence from China’s Economics Efects","authors":"Elena Stavrova, M. Paskaleva, Ani Stoykova","doi":"10.12955/peb.v1.30","DOIUrl":"https://doi.org/10.12955/peb.v1.30","url":null,"abstract":"The prognosis of upcoming crises and the course of actually understanding them is increasingly becoming a major subject of discussions in pursuit of reliable indicators The trade war between the United States and China, along with the COVID-19 pandemic are two events that took place in the Chinese economy with the aforementioned characteristics of the Black swan phenomenon, to which this latest professional analysis is devoted The objective of this research is to examine the response of the Shanghai Stock Exchange Composite (SSEC) index, in addition to its relation with macroeconomic variables contributing towards a possible Black Swan Event We employ an econometric methodology comprising of a unit root test, descriptive statistics, linear regression and correlation analysis for the period 2007-2019 Our results illustarte that the bubble from 2015, which is classified as a Black Swan event by many researchers, has a negative influence on the SSEC index We can further deduce that there were some psychological effects on the Chinese stock market that lead to both, positive and negative trends of SSEC indices The main findings confirmed that the Consumer Price Index, Exchange Rate, Interest Rate, Unemployment, GDP and Trade Balance were significantly elaborative macroeconomic variables, that had a substantial impact on the SSEC index","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80976961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the past twenty years there has 1 been a large inflow of investment capital into commodity futures markets-the financialization of commodities. This chapter analyses the behavior of commodity futures contract returns before and since finalization of the markets. We believe that Professor Gordon C. Rausser's research in the 1970s contributed to the dramatic inflow of speculative investment into commodity futures, because he showed there were possible profits to be made "right at the edge of randomness" with computerized trading rules. Using the methodology in Carter, Rausser and Schmitz (1983) we find that the financialization impacted the Keynesian risk premiums in the futures market, as the market became over-crowded with speculative money.
在过去的二十年里，有大量的投资资本流入商品期货市场——商品金融化。本章分析了商品期货合约入市前和入市后的收益行为。我们认为，戈登·c·劳瑟(Gordon C. Rausser)教授在上世纪70年代的研究促成了投机性投资大量涌入大宗商品期货，因为他表明，在计算机化的交易规则下，“在随机性的边缘”是有可能获利的。使用Carter, Rausser和Schmitz(1983)的方法，我们发现金融化影响了期货市场中的凯恩斯风险溢价，因为市场变得过度拥挤投机资金。
{"title":"The Theory of Normal Backwardization Financialization of the Futures Markets","authors":"C. Carter, C. R. Giha","doi":"10.2139/ssrn.3798704","DOIUrl":"https://doi.org/10.2139/ssrn.3798704","url":null,"abstract":"Over the past twenty years there has 1 been a large inflow of investment capital into commodity futures markets-the financialization of commodities. This chapter analyses the behavior of commodity futures contract returns before and since finalization of the markets. We believe that Professor Gordon C. Rausser's research in the 1970s contributed to the dramatic inflow of speculative investment into commodity futures, because he showed there were possible profits to be made \"right at the edge of randomness\" with computerized trading rules. Using the methodology in Carter, Rausser and Schmitz (1983) we find that the financialization impacted the Keynesian risk premiums in the futures market, as the market became over-crowded with speculative money.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78415720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By studying the differences between futures prices and exchange-traded fund prices for the S&P 500 index, original results are obtained about the distribution and persistence of the microstructure noise component created by bid/ask spreads and discrete price scales. The bivariate density of this component for futures and ETF prices is estimated from high-frequency prices, to provide estimates of the marginal noise densities and measures of noise dependence across the markets studied. Properties of the residual microstructure noise, created by factors other than discrete prices, are also estimated. The residual component has more variation and less persistence than the discrete-price component during the period examined, from January 2010 to December 2012.
{"title":"S&P 500 Microstructure Noise Components: Empirical Inferences from Futures and ETF prices","authors":"Stephen J. Taylor","doi":"10.2139/ssrn.2435853","DOIUrl":"https://doi.org/10.2139/ssrn.2435853","url":null,"abstract":"By studying the differences between futures prices and exchange-traded fund prices for the S&P 500 index, original results are obtained about the distribution and persistence of the microstructure noise component created by bid/ask spreads and discrete price scales. The bivariate density of this component for futures and ETF prices is estimated from high-frequency prices, to provide estimates of the marginal noise densities and measures of noise dependence across the markets studied. Properties of the residual microstructure noise, created by factors other than discrete prices, are also estimated. The residual component has more variation and less persistence than the discrete-price component during the period examined, from January 2010 to December 2012.<br>","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87782989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper attempts to research in two parts, the first part aims to study the Herding Behavior in the sectors of the Egyptian Stock Exchange, when the second part aims to study the factors that affect the Herding Behavior according to the identification of those factors. Factors based on the presentation of previous literature related to herd behavior, and these factors are represented in the exchange rate, Stock trading volumes as an indicator of Liquidity, stock market returns, and indicators of the spread of the Corona virus represented in the number of cumulative cases and deaths according to the population in Egypt. During the period from 1/3/2020 to 31/7/2020. Sectors are five sectors of 76 companies, that have dispersion decrease (CSSD), and the sectors are (Basic Resources, Banks, Travel & Leisure, Health Care & Pharmaceuticals, Food, Beverages and Tobacco). The results of the multiple regression models for the sectors in which herd behavior appeared were as follows: - Basic resources determination coefficient (R2) (52.24%), and the variables determining herd behavior in the sector are variables (Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Banks determination coefficient (R2) (66.85%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Travel & Leisure determination coefficient (R2) (49.04%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Exchange Rate, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Health & Care Pharmaceuticals determination coefficient (R2) (41.84%), and the variables determining herd behavior in the sector are variables (Exchange Rate, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Food, Beverages and Tobacco determination coefficient (R2) (58.87%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).
{"title":"Determinants of Herding Behavior in The Time Of COVID-19: The Case of Egyptian Stock Market Sectors","authors":"Samira Allam, Mansour Abdelrhim, Mahmoud Mohamed","doi":"10.2139/ssrn.3717995","DOIUrl":"https://doi.org/10.2139/ssrn.3717995","url":null,"abstract":"This paper attempts to research in two parts, the first part aims to study the Herding Behavior in the sectors of the Egyptian Stock Exchange, when the second part aims to study the factors that affect the Herding Behavior according to the identification of those factors. Factors based on the presentation of previous literature related to herd behavior, and these factors are represented in the exchange rate, Stock trading volumes as an indicator of Liquidity, stock market returns, and indicators of the spread of the Corona virus represented in the number of cumulative cases and deaths according to the population in Egypt. During the period from 1/3/2020 to 31/7/2020. Sectors are five sectors of 76 companies, that have dispersion decrease (CSSD), and the sectors are (Basic Resources, Banks, Travel & Leisure, Health Care & Pharmaceuticals, Food, Beverages and Tobacco). The results of the multiple regression models for the sectors in which herd behavior appeared were as follows: - Basic resources determination coefficient (R2) (52.24%), and the variables determining herd behavior in the sector are variables (Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Banks determination coefficient (R2) (66.85%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Travel & Leisure determination coefficient (R2) (49.04%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Exchange Rate, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Health & Care Pharmaceuticals determination coefficient (R2) (41.84%), and the variables determining herd behavior in the sector are variables (Exchange Rate, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Food, Beverages and Tobacco determination coefficient (R2) (58.87%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76972893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the impact of COVID-19 pandemic on bond portfolio flows in Asian emerging market economies. We show that the outbreak of COVID-19 pandemic magnified the impact of sovereign default risk on bond portfolio outflows, it also leaded to a higher volatility of bond portfolio flows in longer term. This finding suggests that proper sovereign credit risk management might be particularly important to prevent negative impact caused by unstable bond portfolio flows during the crises.
{"title":"Please Mind the Spread. Impact of Sovereign Default Risk on Bond Portfolio Flows of Emerging Asian Economies during COVID-19 Pandemic","authors":"J. o, W. Wan","doi":"10.2139/ssrn.3711145","DOIUrl":"https://doi.org/10.2139/ssrn.3711145","url":null,"abstract":"This paper examines the impact of COVID-19 pandemic on bond portfolio flows in Asian emerging market economies. We show that the outbreak of COVID-19 pandemic magnified the impact of sovereign default risk on bond portfolio outflows, it also leaded to a higher volatility of bond portfolio flows in longer term. This finding suggests that proper sovereign credit risk management might be particularly important to prevent negative impact caused by unstable bond portfolio flows during the crises.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91050130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Measuring risk is at the center of modern financial risk management. As the world economy is becoming more complex and standard modeling assumptions are violated, the advanced artificial intelligence solutions may provide the right tools to analyze the global market. In this paper, we provide a novel approach for measuring market risk called Encoded Value-at-Risk (Encoded VaR), which is based on a type of artificial neural network, called Variational Auto-encoders (VAEs). Encoded VaR is a generative model which can be used to reproduce market scenarios from a range of historical cross-sectional stock returns, while increasing the signal-to-noise ratio present in the financial data, and learning the dependency structure of the market without any assumptions about the joint distribution of stock returns. We compare Encoded VaR out-of-sample results with eleven other methods and show that it is competitive to many other well-known VaR algorithms presented in the literature.
{"title":"Encoded Value-at-Risk: A Predictive Machine for Financial Risk Management","authors":"H. Arian, M. Moghimi, Ehsan Tabatabaei, S. Zamani","doi":"10.2139/ssrn.3712704","DOIUrl":"https://doi.org/10.2139/ssrn.3712704","url":null,"abstract":"Measuring risk is at the center of modern financial risk management. As the world economy is becoming more complex and standard modeling assumptions are violated, the advanced artificial intelligence solutions may provide the right tools to analyze the global market. In this paper, we provide a novel approach for measuring market risk called Encoded Value-at-Risk (Encoded VaR), which is based on a type of artificial neural network, called Variational Auto-encoders (VAEs). Encoded VaR is a generative model which can be used to reproduce market scenarios from a range of historical cross-sectional stock returns, while increasing the signal-to-noise ratio present in the financial data, and learning the dependency structure of the market without any assumptions about the joint distribution of stock returns. We compare Encoded VaR out-of-sample results with eleven other methods and show that it is competitive to many other well-known VaR algorithms presented in the literature.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84323551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Changes in credit supply induce large and frequent variations in households' access to unsecured debt. They generate a novel financial precautionary motive, which compounds the classical motive associated with idiosyncratic income risk, as borrowers accumulate risk-free bonds to hedge against them. Using a structural model, I estimate that this motive is an important driver of Treasury rates over the business cycle. It explains the historically low level of real rates in the last decade despite consumption growth, solving a "post-Great Recession risk-free rate puzzle". It is also critical for the volatility and comovement of household balance sheet and macroeconomic moments.
{"title":"Unsecured Credit Supply Risk and Bond Prices","authors":"P. Mabille","doi":"10.2139/ssrn.3725031","DOIUrl":"https://doi.org/10.2139/ssrn.3725031","url":null,"abstract":"Changes in credit supply induce large and frequent variations in households' access to unsecured debt. They generate a novel financial precautionary motive, which compounds the classical motive associated with idiosyncratic income risk, as borrowers accumulate risk-free bonds to hedge against them. Using a structural model, I estimate that this motive is an important driver of Treasury rates over the business cycle. It explains the historically low level of real rates in the last decade despite consumption growth, solving a \"post-Great Recession risk-free rate puzzle\". It is also critical for the volatility and comovement of household balance sheet and macroeconomic moments.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87103987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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