M. Bondioli, Martin Goldberg, Nan Hu, Chengrui Li, Olfa Maalaoui Chun, Harvey J. Stein
The DRSK public model estimates forward-looking real-world default probabilities for publicly traded firms. The model also assigns credit grades based on the estimated default probabilities. The product covers firms in all regions and sectors of operation for which the necessary data is available. The DRSK public model was last updated in 2015. This year we are releasing an updated model which improves on the previous model's performance in a variety of ways. The new model's accuracy ratio is above 92%, adjusted pseudo R-squareds have improved, and performance is more in line with observed historical default rates. We describe the new model, analyze its performance in various ways and compare it to the previous model.
{"title":"The Bloomberg Corporate Default Risk Model (DRSK) for Public Firms","authors":"M. Bondioli, Martin Goldberg, Nan Hu, Chengrui Li, Olfa Maalaoui Chun, Harvey J. Stein","doi":"10.2139/ssrn.3911300","DOIUrl":"https://doi.org/10.2139/ssrn.3911300","url":null,"abstract":"The DRSK public model estimates forward-looking real-world default probabilities for publicly traded firms. The model also assigns credit grades based on the estimated default probabilities. The product covers firms in all regions and sectors of operation for which the necessary data is available. The DRSK public model was last updated in 2015. This year we are releasing an updated model which improves on the previous model's performance in a variety of ways. The new model's accuracy ratio is above 92%, adjusted pseudo R-squareds have improved, and performance is more in line with observed historical default rates. We describe the new model, analyze its performance in various ways and compare it to the previous model.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84264359","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 develop a theoretical model to study investors' trading behavior in the presence of large shareholders' influence on a firm's equity. We show that, for a good stock, large shareholders may invest a higher proportion of their wealth in the firm than smart small investors, although they predict the same equity return. Insight is also cast into the impacts of board structure on the firm's equity when the firm possesses several large influential shareholders: (i) the large shareholders collude in trading, and each tends to invest more aggressively as other large shareholders do, and (ii) firms with sole ownership can outperform those with dispersed ownership, if the impact coefficient of the former case exceeds or coincides with the aggregated impact coefficients of the latter.
{"title":"Large Shareholder Premium","authors":"Weihua Huang, Chenghu Ma, Yuhong Xu","doi":"10.2139/ssrn.3780493","DOIUrl":"https://doi.org/10.2139/ssrn.3780493","url":null,"abstract":"We develop a theoretical model to study investors' trading behavior in the presence of large shareholders' influence on a firm's equity. We show that, for a good stock, large shareholders may invest a higher proportion of their wealth in the firm than smart small investors, although they predict the same equity return. Insight is also cast into the impacts of board structure on the firm's equity when the firm possesses several large influential shareholders: (i) the large shareholders collude in trading, and each tends to invest more aggressively as other large shareholders do, and (ii) firms with sole ownership can outperform those with dispersed ownership, if the impact coefficient of the former case exceeds or coincides with the aggregated impact coefficients of the latter.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78532493","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 identifies new currency risk stemming from a network of idiosyncratic option-based currency volatilities and shows how such network risk is priced in the cross-section of currency returns. A portfolio that buys net-receivers and sells net-transmitters of short-term linkages between currency volatilities generates a significant Sharpe ratio. The network strategy formed on causal connections is uncorrelated with popular benchmarks and generates a significant alpha, while network returns formed on aggregate connections, which are driven by a strong correlation component, are partially subsumed by standard factors. Long-term linkages are priced less, indicating a downward-sloping term structure of network risk.
{"title":"Currency Network Risk","authors":"M. Babiak, Jozef Baruník","doi":"10.2139/ssrn.3772245","DOIUrl":"https://doi.org/10.2139/ssrn.3772245","url":null,"abstract":"This paper identifies new currency risk stemming from a network of idiosyncratic option-based currency volatilities and shows how such network risk is priced in the cross-section of currency returns. A portfolio that buys net-receivers and sells net-transmitters of short-term linkages between currency volatilities generates a significant Sharpe ratio. The network strategy formed on causal connections is uncorrelated with popular benchmarks and generates a significant alpha, while network returns formed on aggregate connections, which are driven by a strong correlation component, are partially subsumed by standard factors. Long-term linkages are priced less, indicating a downward-sloping term structure of network risk.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86327236","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 speciality of determining stock prices has been a troublesome task for many researchers and examiners. Indeed, financial specialists are profoundly intrigued by the examination region of stock value prediction. For decent and useful speculation, numerous speculators are sharp in knowing the stock market's future circumstance. Tremendous and powerful prediction frameworks for stock market help dealers, speculators, and experts give vital data like the stock market's future heading. This work presents a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) way to deal with anticipated stock market files.
{"title":"Long Short-Term Memory (LSTM) Algorithm Based Prediction of Stock Market Exchange","authors":"Karunakar Pothuganti","doi":"10.2139/ssrn.3770184","DOIUrl":"https://doi.org/10.2139/ssrn.3770184","url":null,"abstract":"The speciality of determining stock prices has been a troublesome task for many researchers and examiners. Indeed, financial specialists are profoundly intrigued by the examination region of stock value prediction. For decent and useful speculation, numerous speculators are sharp in knowing the stock market's future circumstance. Tremendous and powerful prediction frameworks for stock market help dealers, speculators, and experts give vital data like the stock market's future heading. This work presents a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) way to deal with anticipated stock market files.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75413199","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}
Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie “unexpected losses.” This leads us to develop a framework for forecasting these losses jointly. In an application to quarterly US data on loan charge-offs from 1985 to 2019, we find that financial-cycle indicators – notably, the debt service ratio and credit-to-GDP gap – deliver reliable real-time forecasts, signalling turning points up to three years in advance. Provisions and capital that reflect such forecasts would help reduce the procyclicality of banks’ loss-absorbing resources.
{"title":"Forecasting Expected and Unexpected Losses","authors":"M. Juselius, Nikola A. Tarashev","doi":"10.2139/ssrn.3764723","DOIUrl":"https://doi.org/10.2139/ssrn.3764723","url":null,"abstract":"Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie “unexpected losses.” This leads us to develop a framework for forecasting these losses jointly. In an application to quarterly US data on loan charge-offs from 1985 to 2019, we find that financial-cycle indicators – notably, the debt service ratio and credit-to-GDP gap – deliver reliable real-time forecasts, signalling turning points up to three years in advance. Provisions and capital that reflect such forecasts would help reduce the procyclicality of banks’ loss-absorbing resources.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88445153","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 discusses the computation of hedging strategies under affine Gaussian GARCH dynamics. The risk-minimization hedging strategy is derived in closed-form and related to minimum variance delta hedging. Several numerical experiments are conducted to investigate the accuracy and properties of the proposed hedging formula, as well as the convergence to its continuous-time counterpart based on the GARCH diffusion limit process. An empirical analysis with S&P 500 option data over 2001-2015 indicates that risk-minimization hedging with the affine Gaussian GARCH model outperforms benchmark delta hedges. Our study also reveals that the variance-dependent pricing kernel contributes to improving the hedging performance.
{"title":"On the computation of hedging strategies in affine GARCH models","authors":"Maciej Augustyniak, A. Badescu","doi":"10.2139/ssrn.3475245","DOIUrl":"https://doi.org/10.2139/ssrn.3475245","url":null,"abstract":"This paper discusses the computation of hedging strategies under affine Gaussian GARCH dynamics. The risk-minimization hedging strategy is derived in closed-form and related to minimum variance delta hedging. Several numerical experiments are conducted to investigate the accuracy and properties of the proposed hedging formula, as well as the convergence to its continuous-time counterpart based on the GARCH diffusion limit process. An empirical analysis with S&P 500 option data over 2001-2015 indicates that risk-minimization hedging with the affine Gaussian GARCH model outperforms benchmark delta hedges. Our study also reveals that the variance-dependent pricing kernel contributes to improving the hedging performance.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87884701","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}
Several procedures to estimate daily risk measures in cryptocurrency markets have been recently proposed in the literature. Among them, procedures taking into account the presence of extreme observations, as well as procedures that include more than a single regime, have performed substantially better than standard methods in terms of volatility and Value-at-Risk forecasting. Three of those procedures are revisited in this paper, and their Value-at-Risk forecasting performance is evaluated using recent cryptocurrency data that includes periods of turbulence. Those procedures are also extended to estimate the Expected Shortfall, and a comprehensive backtesting exercise based on both calibration tests and scoring functions is performed. In order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are robust to outliers performed slightly better than regime-switching models. We found some evidence that combining strategies can improve the forecasting of Value-at-Risk and Expected Shortfall, particularly for the 1% risk levels, making them an interesting alternative to be used by practitioners.
{"title":"Forecasting Value-at-Risk and Expected Shortfall of Cryptocurrencies using Combinations based on Jump-Robust and Regime-Switching Models","authors":"Carlos Trucíos, James W. Taylor","doi":"10.2139/ssrn.3751435","DOIUrl":"https://doi.org/10.2139/ssrn.3751435","url":null,"abstract":"Several procedures to estimate daily risk measures in cryptocurrency markets have been recently proposed in the literature. Among them, procedures taking into account the presence of extreme observations, as well as procedures that include more than a single regime, have performed substantially better than standard methods in terms of volatility and Value-at-Risk forecasting. Three of those procedures are revisited in this paper, and their Value-at-Risk forecasting performance is evaluated using recent cryptocurrency data that includes periods of turbulence. Those procedures are also extended to estimate the Expected Shortfall, and a comprehensive backtesting exercise based on both calibration tests and scoring functions is performed. In order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are robust to outliers performed slightly better than regime-switching models. We found some evidence that combining strategies can improve the forecasting of Value-at-Risk and Expected Shortfall, particularly for the 1% risk levels, making them an interesting alternative to be used by practitioners.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82074395","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}
Abstract Relationships between large customers and suppliers expose lenders to additional risks. These risks may force lead agents to retain a larger share of syndicated loans, reducing loan-level diversification, and, in turn, increasing the required interest rate spread. Consistent with this view, we find that borrowers' dependence on a few larger customers or suppliers positively affects the cost of the loans indirectly through the loan structure. Instead, we do not observe a direct cost associated with large customer-supplier links, suggesting that lead agents do not increase the interest rate spread as compensation for the additional risks of dealing with borrowers with large customer-supplier links per se. Finally, we document an inverted U-shaped relationship between the length of the large customer-supplier link and the loan share held by the lead agent.
{"title":"Large Customer-supplier Links and Syndicate Loan Structure","authors":"E. Croci, Marta Degl'Innocenti, Si Zhou","doi":"10.2139/ssrn.3353214","DOIUrl":"https://doi.org/10.2139/ssrn.3353214","url":null,"abstract":"Abstract Relationships between large customers and suppliers expose lenders to additional risks. These risks may force lead agents to retain a larger share of syndicated loans, reducing loan-level diversification, and, in turn, increasing the required interest rate spread. Consistent with this view, we find that borrowers' dependence on a few larger customers or suppliers positively affects the cost of the loans indirectly through the loan structure. Instead, we do not observe a direct cost associated with large customer-supplier links, suggesting that lead agents do not increase the interest rate spread as compensation for the additional risks of dealing with borrowers with large customer-supplier links per se. Finally, we document an inverted U-shaped relationship between the length of the large customer-supplier link and the loan share held by the lead agent.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77128928","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 investigate the puzzle in the literature that various parametric loss given default (LGD) statistical models perform similarly, by comparing their performance in a simulation framework. We find that, even using the full set of explanatory variables from the assumed data-generating process where noise is minimized, these models still show a similarly poor performance in terms of predictive accuracy and rank-ordering when mean predictions and squared error loss functions are used. However, the sophisticated parametric modes that are specifically designed to address the bimodal distributions of LGD outperform the less sophisticated models by a large margin in terms of predicted distributions. Our results also suggest that stress testing may pose a challenge to all LGD models due to a lack of loss data and the limited availability of relevant explanatory variables, and that model selection criteria based on goodness-of-fit may not serve the stress testing purpose well. Copyright Infopro Digital Limited. All rights reserved.
{"title":"Modeling Loss Given Default Regressions","authors":"Phillip Li, Xiaofei Zhang, Xinlei Zhao","doi":"10.21314/jor.2020.443","DOIUrl":"https://doi.org/10.21314/jor.2020.443","url":null,"abstract":"We investigate the puzzle in the literature that various parametric loss given default (LGD) statistical models perform similarly, by comparing their performance in a simulation framework. We find that, even using the full set of explanatory variables from the assumed data-generating process where noise is minimized, these models still show a similarly poor performance in terms of predictive accuracy and rank-ordering when mean predictions and squared error loss functions are used. However, the sophisticated parametric modes that are specifically designed to address the bimodal distributions of LGD outperform the less sophisticated models by a large margin in terms of predicted distributions. Our results also suggest that stress testing may pose a challenge to all LGD models due to a lack of loss data and the limited availability of relevant explanatory variables, and that model selection criteria based on goodness-of-fit may not serve the stress testing purpose well.<br>Copyright Infopro Digital Limited. All rights reserved.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86048437","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 propose a method for reducing standard errors associated with industry equity capital costs (ECC), a problem studied by Fama French (1997). Approximately 90% of the uncertainty regarding ECC estimates comes from the factor risk premia, as opposed to factor exposures. Furthermore, at least 75% of the uncertainty regarding these risk premia is driven by the standard error of the second pass regression. These standard errors are inflated by seasonal noise in the return process. By filtering this noise, we generate ECC estimates that are unchanged on average, but with standard errors that are about one-quarter of the size without filtering.
{"title":"A Practical Method for Sharpening Estimates of Industry Equity Capital Costs","authors":"Mike Aguilar, Robert A. Connolly, Jiaxia Li","doi":"10.2139/ssrn.3742221","DOIUrl":"https://doi.org/10.2139/ssrn.3742221","url":null,"abstract":"We propose a method for reducing standard errors associated with industry equity capital costs (ECC), a problem studied by Fama French (1997). Approximately 90% of the uncertainty regarding ECC estimates comes from the factor risk premia, as opposed to factor exposures. Furthermore, at least 75% of the uncertainty regarding these risk premia is driven by the standard error of the second pass regression. These standard errors are inflated by seasonal noise in the return process. By filtering this noise, we generate ECC estimates that are unchanged on average, but with standard errors that are about one-quarter of the size without filtering.","PeriodicalId":11410,"journal":{"name":"Econometric Modeling: Capital Markets - Risk eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76089639","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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