Daniel Foos, E. Lütkebohmert, Mariia Markovych, Kamil Pliszka
This paper investigates interest rate risk exposures of listed euro area banks which fall under the Single Supervisory Mechanism (SSM). We analyze the period 2005 to 2014, as it includes times of very low interest rates in which banks may have pursued a more risky maturity transformation strategy. First, we use the Bayesian DCC M-GARCH model to assess banks' stock price sensitivities to principal components of changes in the yield curve describing shifts in its level, slope and curvature. Second, we investigate how these sensitivities vary depending on bank-level characteristics (e.g., balance sheet composition, reliance on interest income). Our findings reveal that, on average, banks benefit from positive level shifts and steepening yield curves. Curvature changes affect banks' share prices as well, particularly in times of crises. Further, these sensitivities change in time and depend heavily on the bank's business model and balance sheet composition. Our analysis reveals that banks with larger balance sheets, higher capital ratios, higher parts of customer loans and lower parts of deposits are particularly sensitive to interest rate movements.
{"title":"Euro Area Banks' Interest Rate Risk Exposure to Level, Slope and Curvature Swings in the Yield Curve","authors":"Daniel Foos, E. Lütkebohmert, Mariia Markovych, Kamil Pliszka","doi":"10.2139/ssrn.3033719","DOIUrl":"https://doi.org/10.2139/ssrn.3033719","url":null,"abstract":"This paper investigates interest rate risk exposures of listed euro area banks which fall under the Single Supervisory Mechanism (SSM). We analyze the period 2005 to 2014, as it includes times of very low interest rates in which banks may have pursued a more risky maturity transformation strategy. First, we use the Bayesian DCC M-GARCH model to assess banks' stock price sensitivities to principal components of changes in the yield curve describing shifts in its level, slope and curvature. Second, we investigate how these sensitivities vary depending on bank-level characteristics (e.g., balance sheet composition, reliance on interest income). Our findings reveal that, on average, banks benefit from positive level shifts and steepening yield curves. Curvature changes affect banks' share prices as well, particularly in times of crises. Further, these sensitivities change in time and depend heavily on the bank's business model and balance sheet composition. Our analysis reveals that banks with larger balance sheets, higher capital ratios, higher parts of customer loans and lower parts of deposits are particularly sensitive to interest rate movements.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125508615","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 introduces a generalizable method to estimate reduced form risk decompositions at daily and intraday frequencies applied to CMBX. We estimate partitions for the risks of default, liquidity, excess liquidity, and interest rate volatility at daily and intraday frequencies. Our new estimation technique combines previously simulated risk partitions with current market data using principal components and OLS methods. We find liquidity and excess liquidity risk partitions are significant in explaining daily effective bid-ask spreads historically, from 11/2007-4/2019, and in 20-day forecasts. During the Covid pandemic, we extend the model from daily to intraday frequency, estimating intraday in 15 second intervals over the period 4/2020-4/2021. During Covid, we find regular patterns of risk partition volatility in the cross-section and exploit those insights in the related, and more frequently traded, REIT sector in automated trading strategies. In our 54 long/short day trading strategies, 96% showed significant alphas, and 63% produced abnormal cumulative returns between 0.73% and 48.74%. These results support pricing risk with risk partitioning at higher frequencies for commercial real estate securities.
{"title":"15 Seconds to Alpha: Higher frequency risk pricing for commercial real estate securities","authors":"A. Christopoulos, J. Barratt","doi":"10.2139/ssrn.3852381","DOIUrl":"https://doi.org/10.2139/ssrn.3852381","url":null,"abstract":"This paper introduces a generalizable method to estimate reduced form risk decompositions at daily and intraday frequencies applied to CMBX. We estimate partitions for the risks of default, liquidity, excess liquidity, and interest rate volatility at daily and intraday frequencies. Our new estimation technique combines previously simulated risk partitions with current market data using principal components and OLS methods. We find liquidity and excess liquidity risk partitions are significant in explaining daily effective bid-ask spreads historically, from 11/2007-4/2019, and in 20-day forecasts. During the Covid pandemic, we extend the model from daily to intraday frequency, estimating intraday in 15 second intervals over the period 4/2020-4/2021. During Covid, we find regular patterns of risk partition volatility in the cross-section and exploit those insights in the related, and more frequently traded, REIT sector in automated trading strategies. In our 54 long/short day trading strategies, 96% showed significant alphas, and 63% produced abnormal cumulative returns between 0.73% and 48.74%. These results support pricing risk with risk partitioning at higher frequencies for commercial real estate securities.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121927205","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}
Banks can take costly actions (such as higher capitalization, liquidity holding, and advanced risk management) to fend off runs. While such actions directly affect bank risks, they can also serve as signals of the banks’ fundamentals. A separating equilibrium due to such signaling, however, would involve two types of inefficiency: strong banks choose excessively costly signals, whereas weak banks are particularly vulnerable to runs. We show that minimum regulatory requirements can maintain a pooling equilibrium and eliminate the inefficiencies associated with the separation. We support this novel rationale for prudential regulations with evidence from the US liquidity requirement.
{"title":"Bank Signaling, Risk of Runs, and the Informational Impact of Prudential Regulations","authors":"Warwick Business School Submitter","doi":"10.2139/ssrn.3902600","DOIUrl":"https://doi.org/10.2139/ssrn.3902600","url":null,"abstract":"Banks can take costly actions (such as higher capitalization, liquidity holding, and advanced risk management) to fend off runs. While such actions directly affect bank risks, they can also serve as signals of the banks’ fundamentals. A separating equilibrium due to such signaling, however, would involve two types of inefficiency: strong banks choose excessively costly signals, whereas weak banks are particularly vulnerable to runs. We show that minimum regulatory requirements can maintain a pooling equilibrium and eliminate the inefficiencies associated with the separation. We support this novel rationale for prudential regulations with evidence from the US liquidity requirement.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114803665","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}
Alexander Blasberg, Ruediger Kiesel, Luca Taschini
The substantial economic transformation required to mitigate and adapt to climate change will lower the value of certain businesses as well as some firms' assets in the not-too-distant future. Firms will need to transition to a less carbon-intensive business model, but may do so at different times and at different speeds, incurring different costs and risks in the process. We propose and implement a novel market-based measure of exposure to transition risk (transition risk factor) and examine how this risk affects firms' creditworthiness. We discipline the exercise by using Credit Default Swap (CDS) spreads to capture differential exposure to transition risk across economic sectors. We show that the transition risk factor is a relevant determinant of CDS spreads and provide evidence of the relationship between the differential exposure to transition risk and firms' cost of default protection. This effect is particularly pronounced during deteriorating credit market movements. However, effects vary substantially across industries, reflecting the fact that transition risk impacts firms' valuation differently depending on their sector. Our findings also suggest that investors seek greater protection against transition risks in the short– to medium-term, indicating an expectation of a swift transformation of the entire economic structure.
{"title":"Climate Default Swap – Disentangling the Exposure to Transition Risk Through CDS","authors":"Alexander Blasberg, Ruediger Kiesel, Luca Taschini","doi":"10.2139/ssrn.3856993","DOIUrl":"https://doi.org/10.2139/ssrn.3856993","url":null,"abstract":"The substantial economic transformation required to mitigate and adapt to climate change will lower the value of certain businesses as well as some firms' assets in the not-too-distant future. Firms will need to transition to a less carbon-intensive business model, but may do so at different times and at different speeds, incurring different costs and risks in the process. We propose and implement a novel market-based measure of exposure to transition risk (transition risk factor) and examine how this risk affects firms' creditworthiness. We discipline the exercise by using Credit Default Swap (CDS) spreads to capture differential exposure to transition risk across economic sectors. We show that the transition risk factor is a relevant determinant of CDS spreads and provide evidence of the relationship between the differential exposure to transition risk and firms' cost of default protection. This effect is particularly pronounced during deteriorating credit market movements. However, effects vary substantially across industries, reflecting the fact that transition risk impacts firms' valuation differently depending on their sector. Our findings also suggest that investors seek greater protection against transition risks in the short– to medium-term, indicating an expectation of a swift transformation of the entire economic structure.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133393264","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}
Pietro Grandi, Jean-Jacques Belin, Elisa Darriet, M. Guille
We study how private R&D investment in France was affected by the tightening of credit conditions during the European Sovereign Debt Crisis. Using detailed R&D information on more than 25000 French companies, we show that financially constrained firms were relatively more likely to scale back their R&D activities following the sovereign debt crisis. We then focus on bank-firm linkages and exploit variation in the sovereign risk exposure of firms’ main bank during the sovereign debt crisis as an exogenous credit supply shock. Results indicate that firms related to banks with larger exposures to risky sovereign debt decreased R&D expenditure by more relative to other firms following the crisis. Our findings indicate that credit supply shocks have significant impact on firms’ R&D activities, and highlight an important transmission channel of sovereign risk to firm innovation and productivity.
{"title":"Sovereign Risk, Credit Shocks and R&D","authors":"Pietro Grandi, Jean-Jacques Belin, Elisa Darriet, M. Guille","doi":"10.2139/ssrn.3780062","DOIUrl":"https://doi.org/10.2139/ssrn.3780062","url":null,"abstract":"We study how private R&D investment in France was affected by the tightening of credit conditions during the European Sovereign Debt Crisis. Using detailed R&D information on more than 25000 French companies, we show that financially constrained firms were relatively more likely to scale back their R&D activities following the sovereign debt crisis. We then focus on bank-firm linkages and exploit variation in the sovereign risk exposure of firms’ main bank during the sovereign debt crisis as an exogenous credit supply shock. Results indicate that firms related to banks with larger exposures to risky sovereign debt decreased R&D expenditure by more relative to other firms following the crisis. Our findings indicate that credit supply shocks have significant impact on firms’ R&D activities, and highlight an important transmission channel of sovereign risk to firm innovation and productivity.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130292362","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}
Risk is a vital concept to grasp when investing in a firm or project. It is also a key ingredient required to evaluate the cost of capital and perform a valuation. An organization’s capital structure, specifically the amount of leverage and debt financing employed, must be accounted for to correctly assess a project’s risk.
There are different measures of risk used by practitioners. The most widely used risk measure corporate finance is CAPM beta. It can be calculated as the co-movement of returns with the market and/or equivalently as the slope of a regression analysis. CAPM beta measures a firm’s exposure to systematic risk and assumes that investors are not rewarded for firm specific risk (Berk and DeMarzo, 2016).
Many firms and projects are illiquid and/or have no public data. In such cases we measure and imply beta risk from comparable company data. However risk increases with leverage and the level of debt financing used to finance the company or project (Brealey et al, 2014). Consequently, we are required to unlever and relever betas to remove leverage effects from the comparable company and add the leverage effects of the target company to give a reliable indicator of beta risk (Koller et al, 2015).
Not only is CAPM beta useful to assess the risk of a firm or project, but it is essential to calculate the weighted average cost of capital (WACC). It is the expected return investors require to invest in a project and incorporate the correct level of risk. The WACC is required to value of a firm or project and perform a discounted cash flows (DCF) analysis, see (Burgess 2020a), (Burgess 2020b) and (Burgess 2020c).
{"title":"How Risky is your Project Really? Corporate Finance Strategies for Assessing Risk","authors":"N. Burgess","doi":"10.2139/ssrn.3748822","DOIUrl":"https://doi.org/10.2139/ssrn.3748822","url":null,"abstract":"Risk is a vital concept to grasp when investing in a firm or project. It is also a key ingredient required to evaluate the cost of capital and perform a valuation. An organization’s capital structure, specifically the amount of leverage and debt financing employed, must be accounted for to correctly assess a project’s risk.<br><br>There are different measures of risk used by practitioners. The most widely used risk measure corporate finance is CAPM beta. It can be calculated as the co-movement of returns with the market and/or equivalently as the slope of a regression analysis. CAPM beta measures a firm’s exposure to systematic risk and assumes that investors are not rewarded for firm specific risk (Berk and DeMarzo, 2016). <br><br>Many firms and projects are illiquid and/or have no public data. In such cases we measure and imply beta risk from comparable company data. However risk increases with leverage and the level of debt financing used to finance the company or project (Brealey et al, 2014). Consequently, we are required to unlever and relever betas to remove leverage effects from the comparable company and add the leverage effects of the target company to give a reliable indicator of beta risk (Koller et al, 2015).<br><br>Not only is CAPM beta useful to assess the risk of a firm or project, but it is essential to calculate the weighted average cost of capital (WACC). It is the expected return investors require to invest in a project and incorporate the correct level of risk. The WACC is required to value of a firm or project and perform a discounted cash flows (DCF) analysis, see (Burgess 2020a), (Burgess 2020b) and (Burgess 2020c).<br>","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129092425","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 studies the performance of the portfolios based on the Hierarchical Equal Risk Contribution algorithm in China stock market. Specifically, we consider a variety of risk measures for calculating weight allocations which include equal weighting, variance, standard deviation, expected shortfall and conditional draw-down risk and four types of linkage criteria used for agglomerative clustering, namely, single, complete, average, and Ward linkages. We compare the performance of the portfolios based on the HERC algorithm to the equal-weighted and inverse-variance portfolios. We find that most HERC portfolios are not able to beat the equal-weighted and inverse-variance portfolios in terms of several comparison measures and HERC with Ward-linkage seems to dominate the ones with other linkages. However, the results do not show that any risk measures can beat other measures consistently.
{"title":"Performance of Hierarchical Equal Risk Contribution Algorithm in China Market","authors":"Weige Huang","doi":"10.2139/ssrn.3695598","DOIUrl":"https://doi.org/10.2139/ssrn.3695598","url":null,"abstract":"This paper studies the performance of the portfolios based on the Hierarchical Equal Risk Contribution algorithm in China stock market. Specifically, we consider a variety of risk measures for calculating weight allocations which include equal weighting, variance, standard deviation, expected shortfall and conditional draw-down risk and four types of linkage criteria used for agglomerative clustering, namely, single, complete, average, and Ward linkages. We compare the performance of the portfolios based on the HERC algorithm to the equal-weighted and inverse-variance portfolios. We find that most HERC portfolios are not able to beat the equal-weighted and inverse-variance portfolios in terms of several comparison measures and HERC with Ward-linkage seems to dominate the ones with other linkages. However, the results do not show that any risk measures can beat other measures consistently.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180293","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 study attempts to contribute to the literature on risk exposures by investigating the dynamic volatility spillover transmissions and volatility co-movements between oil-risk factors and sectoral stocks in BRICS countries. A spillover index and DCC-GARCH estimation techniques are applied to identify the volatility transmission mechanism and co-movement among the series using daily data from 5th May 2007 to December 31st, 2016. To provide practical implications of the volatility transmissions, the estimated results are in turn used to compute and analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. Our findings indicate the existence of significant volatility spillover interdependences and a time-varying volatility co-movement between oil-risk factors and sectoral stocks. However, the direction of spillover is shown to be somewhat unidirectional mainly from some selected sectors to oil-risk factors. Thus, we show that although volatility spillovers from oil-risk factors to sectors exist, the effect is at best, marginal. Finally, the optimal weights and hedge ratios show that oil-risk factors would be better suited as instruments for portfolio diversification rather than hedging to minimize oil price and portfolio risks which is important for risk management and diversification benefits.
{"title":"Risk Dynamics of Sectoral Stocks in BRICS Countries","authors":"K. Dogah, G. Premaratne","doi":"10.2139/ssrn.3700634","DOIUrl":"https://doi.org/10.2139/ssrn.3700634","url":null,"abstract":"This study attempts to contribute to the literature on risk exposures by investigating the dynamic volatility spillover transmissions and volatility co-movements between oil-risk factors and sectoral stocks in BRICS countries. A spillover index and DCC-GARCH estimation techniques are applied to identify the volatility transmission mechanism and co-movement among the series using daily data from 5th May 2007 to December 31st, 2016. To provide practical implications of the volatility transmissions, the estimated results are in turn used to compute and analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. Our findings indicate the existence of significant volatility spillover interdependences and a time-varying volatility co-movement between oil-risk factors and sectoral stocks. However, the direction of spillover is shown to be somewhat unidirectional mainly from some selected sectors to oil-risk factors. Thus, we show that although volatility spillovers from oil-risk factors to sectors exist, the effect is at best, marginal. Finally, the optimal weights and hedge ratios show that oil-risk factors would be better suited as instruments for portfolio diversification rather than hedging to minimize oil price and portfolio risks which is important for risk management and diversification benefits.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114941151","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}
Robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance when making well-informed risk management decisions. In this paper, we quantify for any given distortion risk measure its robustness to distributional uncertainty by deriving its range of attainable values when the underlying loss distribution has a known mean and variance and furthermore lies within a ball - specified through the Wasserstein distance - around a reference distribution. We extend our results to account for uncertainty in the first two moments and provide an application to model risk assessment.
{"title":"Robust Distortion Risk Measures","authors":"C. Bernard, Silvana M. Pesenti, S. Vanduffel","doi":"10.2139/ssrn.3677078","DOIUrl":"https://doi.org/10.2139/ssrn.3677078","url":null,"abstract":"Robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance when making well-informed risk management decisions. In this paper, we quantify for any given distortion risk measure its robustness to distributional uncertainty by deriving its range of attainable values when the underlying loss distribution has a known mean and variance and furthermore lies within a ball - specified through the Wasserstein distance - around a reference distribution. We extend our results to account for uncertainty in the first two moments and provide an application to model risk assessment.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126544783","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}
We document changes in borrowers’ sensitivity to negative equity and show heightened borrower default propensity as a fundamental driver of crisis period mortgage defaults. Estimates of a time-varying coefficient competing risk hazard model reveal a marked run-up in the default option beta from 0.2 during 2003–06 to about 1.5 during 2012–13. Simulation of 2006 vintage loan performance shows that the marked upturn in the default option beta resulted in a doubling of mortgage default incidence. Panel data analysis indicates that much of the variation in default option exercise is associated with the local business cycle and consumer distress. Results also indicate elevated default propensities in sand states and among borrowers seeking a crisis-period Home Affordable Modification Program loan modification.
{"title":"Default Option Exercise Over the Financial Crisis and Beyond","authors":"Xudong An, Yongheng Deng, S. Gabriel","doi":"10.2139/ssrn.2764026","DOIUrl":"https://doi.org/10.2139/ssrn.2764026","url":null,"abstract":"\u0000 We document changes in borrowers’ sensitivity to negative equity and show heightened borrower default propensity as a fundamental driver of crisis period mortgage defaults. Estimates of a time-varying coefficient competing risk hazard model reveal a marked run-up in the default option beta from 0.2 during 2003–06 to about 1.5 during 2012–13. Simulation of 2006 vintage loan performance shows that the marked upturn in the default option beta resulted in a doubling of mortgage default incidence. Panel data analysis indicates that much of the variation in default option exercise is associated with the local business cycle and consumer distress. Results also indicate elevated default propensities in sand states and among borrowers seeking a crisis-period Home Affordable Modification Program loan modification.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131741299","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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