�ݺ�ߣshows by User: MikeJacobs

�ݺ�ߣshows by User: MikeJacobs / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: MikeJacobs / Thu, 15 Aug 2013 06:57:07 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: MikeJacobs Jacobs stress testing_aug13_8-15-13_v4 /slideshow/jacobs-stress-testingaug1381513v4/25270170 jacobsstresstestingaug138-15-13v4-130815065707-phpapp01
In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach. ]]>
In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach. ]]> Thu, 15 Aug 2013 06:57:07 GMT /slideshow/jacobs-stress-testingaug1381513v4/25270170 MikeJacobs@slideshare.net(MikeJacobs) Jacobs stress testing_aug13_8-15-13_v4 MikeJacobs In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsstresstestingaug138-15-13v4-130815065707-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach.

Jacobs stress testing_aug13_8-15-13_v4 from Michael Jacobs, Jr.

]]> 2095 10 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsstresstestingaug138-15-13v4-130815065707-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Reg Frmwrks Mkt Risk Presentation Julu12 7 15 12 V5 /slideshow/jacobs-reg-frmwrks-mkt-risk-presentation-julu12-7-15-12-v5/13871850 jacobsregfrmwrksmktriskpresentationjulu1271512v5-13440980334423-phpapp02-120804113454-phpapp02
This presentation siscussses the new market risk regulations.]]>
This presentation siscussses the new market risk regulations.]]> Sat, 04 Aug 2012 11:34:39 GMT /slideshow/jacobs-reg-frmwrks-mkt-risk-presentation-julu12-7-15-12-v5/13871850 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Reg Frmwrks Mkt Risk Presentation Julu12 7 15 12 V5 MikeJacobs This presentation siscussses the new market risk regulations. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsregfrmwrksmktriskpresentationjulu1271512v5-13440980334423-phpapp02-120804113454-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation siscussses the new market risk regulations.

Jacobs Reg Frmwrks Mkt Risk Presentation Julu12 7 15 12 V5 from Michael Jacobs, Jr.

]]> 552 4 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsregfrmwrksmktriskpresentationjulu1271512v5-13440980334423-phpapp02-120804113454-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Dodd Frank&Basel3 July12 7 15 12 V16 /slideshow/jacobs-dodd-frankampbasel3-july12-7-15-12-v16/13871830 jacobsdoddfrankbasel3july1271512v16-13440978554917-phpapp01-120804113247-phpapp01
This presentation discusses the new market risk rukles under basel III and the Dodd-Frank regulatory reform.]]>
This presentation discusses the new market risk rukles under basel III and the Dodd-Frank regulatory reform.]]> Sat, 04 Aug 2012 11:32:16 GMT /slideshow/jacobs-dodd-frankampbasel3-july12-7-15-12-v16/13871830 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Dodd Frank&Basel3 July12 7 15 12 V16 MikeJacobs This presentation discusses the new market risk rukles under basel III and the Dodd-Frank regulatory reform. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsdoddfrankbasel3july1271512v16-13440978554917-phpapp01-120804113247-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation discusses the new market risk rukles under basel III and the Dodd-Frank regulatory reform.

Jacobs Dodd Frank&Basel3 July12 7 15 12 V16 from Michael Jacobs, Jr.

]]> 590 7 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsdoddfrankbasel3july1271512v16-13440978554917-phpapp01-120804113247-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Empirical Analysis of Bank Capital and New Regulatory Requirements for Risks in Trading Portfolios /slideshow/empirical-analysis-of-bank-capital-and-new-regulatory-requirements-for-risks-in-trading-portfolios/13437085 inanoglujacobskaragozogluircpresentationirmcjune1261112v1-13405575146319-phpapp02-120624120728-phpapp02
We examine the impact of new supervisory standards for bank trading portfolios, additional capital requirements for liquidity risk and credit risk (the Incremental Risk Charge), introduced under Basel 2.5. We estimate risk measures under alternative assumptions on portfolio dynamics (constant level of risk vs. constant positions), rating systems (through-the-cycle vs. point-in-time), for different sectors (asset classes and industry groups), alternative credit risk frameworks (al-ternative dependency structures or factor models) and an extension to a Bayesian framework. We find a potentially material increase in capital requirements, above and beyond that concluded in the far-ranging impact studies conducted by the international supervisors utilizing the participation of a large sample of banks. Results indicate that capital charges are in general higher for either point-in-time ratings or constant portfolio dynamics, with this effect accentuated for financial or sovereign as compared to industrial sectors; and that regulatory is larger than economic capital for the latter, but not for the former sectors. A comparison of the single to a multi-factor credit models shows that capital estimates larger in the latter, and for the financial / sovereign by orders of magnitude vs. industrial or the Basel II model, and that there is less sensitivity of results across sectors and rating systems as compared with the single factor model. Furthermore, in a Bayesian experiment we find that the new requirements may introduce added uncertainty into risk measures as compared to existing approaches.]]>
We examine the impact of new supervisory standards for bank trading portfolios, additional capital requirements for liquidity risk and credit risk (the Incremental Risk Charge), introduced under Basel 2.5. We estimate risk measures under alternative assumptions on portfolio dynamics (constant level of risk vs. constant positions), rating systems (through-the-cycle vs. point-in-time), for different sectors (asset classes and industry groups), alternative credit risk frameworks (al-ternative dependency structures or factor models) and an extension to a Bayesian framework. We find a potentially material increase in capital requirements, above and beyond that concluded in the far-ranging impact studies conducted by the international supervisors utilizing the participation of a large sample of banks. Results indicate that capital charges are in general higher for either point-in-time ratings or constant portfolio dynamics, with this effect accentuated for financial or sovereign as compared to industrial sectors; and that regulatory is larger than economic capital for the latter, but not for the former sectors. A comparison of the single to a multi-factor credit models shows that capital estimates larger in the latter, and for the financial / sovereign by orders of magnitude vs. industrial or the Basel II model, and that there is less sensitivity of results across sectors and rating systems as compared with the single factor model. Furthermore, in a Bayesian experiment we find that the new requirements may introduce added uncertainty into risk measures as compared to existing approaches.]]> Sun, 24 Jun 2012 12:06:34 GMT /slideshow/empirical-analysis-of-bank-capital-and-new-regulatory-requirements-for-risks-in-trading-portfolios/13437085 MikeJacobs@slideshare.net(MikeJacobs) Empirical Analysis of Bank Capital and New Regulatory Requirements for Risks in Trading Portfolios MikeJacobs We examine the impact of new supervisory standards for bank trading portfolios, additional capital requirements for liquidity risk and credit risk (the Incremental Risk Charge), introduced under Basel 2.5. We estimate risk measures under alternative assumptions on portfolio dynamics (constant level of risk vs. constant positions), rating systems (through-the-cycle vs. point-in-time), for different sectors (asset classes and industry groups), alternative credit risk frameworks (al-ternative dependency structures or factor models) and an extension to a Bayesian framework. We find a potentially material increase in capital requirements, above and beyond that concluded in the far-ranging impact studies conducted by the international supervisors utilizing the participation of a large sample of banks. Results indicate that capital charges are in general higher for either point-in-time ratings or constant portfolio dynamics, with this effect accentuated for financial or sovereign as compared to industrial sectors; and that regulatory is larger than economic capital for the latter, but not for the former sectors. A comparison of the single to a multi-factor credit models shows that capital estimates larger in the latter, and for the financial / sovereign by orders of magnitude vs. industrial or the Basel II model, and that there is less sensitivity of results across sectors and rating systems as compared with the single factor model. Furthermore, in a Bayesian experiment we find that the new requirements may introduce added uncertainty into risk measures as compared to existing approaches. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/inanoglujacobskaragozogluircpresentationirmcjune1261112v1-13405575146319-phpapp02-120624120728-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> We examine the impact of new supervisory standards for bank trading portfolios, additional capital requirements for liquidity risk and credit risk (the Incremental Risk Charge), introduced under Basel 2.5. We estimate risk measures under alternative assumptions on portfolio dynamics (constant level of risk vs. constant positions), rating systems (through-the-cycle vs. point-in-time), for different sectors (asset classes and industry groups), alternative credit risk frameworks (al-ternative dependency structures or factor models) and an extension to a Bayesian framework. We find a potentially material increase in capital requirements, above and beyond that concluded in the far-ranging impact studies conducted by the international supervisors utilizing the participation of a large sample of banks. Results indicate that capital charges are in general higher for either point-in-time ratings or constant portfolio dynamics, with this effect accentuated for financial or sovereign as compared to industrial sectors; and that regulatory is larger than economic capital for the latter, but not for the former sectors. A comparison of the single to a multi-factor credit models shows that capital estimates larger in the latter, and for the financial / sovereign by orders of magnitude vs. industrial or the Basel II model, and that there is less sensitivity of results across sectors and rating systems as compared with the single factor model. Furthermore, in a Bayesian experiment we find that the new requirements may introduce added uncertainty into risk measures as compared to existing approaches.

Empirical Analysis of Bank Capital and New Regulatory Requirements for Risks in Trading Portfolios from Michael Jacobs, Jr.

]]> 757 9 https://cdn.slidesharecdn.com/ss_thumbnails/inanoglujacobskaragozogluircpresentationirmcjune1261112v1-13405575146319-phpapp02-120624120728-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Str Tst Crdt Prtfl Risk Mar2012 3 22 12 V20 Nomacr /slideshow/jacobs-str-tst-crdt-prtfl-risk-mar2012-3-22-12-v20-nomacr/12122325 jacobsstrtstcrdtprtflriskmar201232212v20nomacr-13324573083183-phpapp01-120322180326-phpapp01
Modern credit risk modeling (e.g., Merton, 1974) increasingly relies on advanced mathematical, statistical and numerical echniques to measure and manage risk in redit portfolios This gives rise to model risk (OCC 2011-16) and the possibility of nderstating nherent dangers stemming from very rare yet plausible occurrencs perhaps not in our eference data-sets International supervisors have recognized the importance of stress testing credit risk in the Basel framework (BCBS, 2009) It can and has been argued that the art and science of stress testing has lagged in the domain of credit, vs. other types of risk (e.g., market), and our objective is to help fill this vacuum We aim to present classifications & established techniques that will help practitioners formulate robust credit risk stress tests]]>
Modern credit risk modeling (e.g., Merton, 1974) increasingly relies on advanced mathematical, statistical and numerical echniques to measure and manage risk in redit portfolios This gives rise to model risk (OCC 2011-16) and the possibility of nderstating nherent dangers stemming from very rare yet plausible occurrencs perhaps not in our eference data-sets International supervisors have recognized the importance of stress testing credit risk in the Basel framework (BCBS, 2009) It can and has been argued that the art and science of stress testing has lagged in the domain of credit, vs. other types of risk (e.g., market), and our objective is to help fill this vacuum We aim to present classifications & established techniques that will help practitioners formulate robust credit risk stress tests]]> Thu, 22 Mar 2012 18:03:07 GMT /slideshow/jacobs-str-tst-crdt-prtfl-risk-mar2012-3-22-12-v20-nomacr/12122325 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Str Tst Crdt Prtfl Risk Mar2012 3 22 12 V20 Nomacr MikeJacobs Modern credit risk modeling (e.g., Merton, 1974) increasingly relies on advanced mathematical, statistical and numerical echniques to measure and manage risk in redit portfolios This gives rise to model risk (OCC 2011-16) and the possibility of nderstating nherent dangers stemming from very rare yet plausible occurrencs perhaps not in our eference data-sets International supervisors have recognized the importance of stress testing credit risk in the Basel framework (BCBS, 2009) It can and has been argued that the art and science of stress testing has lagged in the domain of credit, vs. other types of risk (e.g., market), and our objective is to help fill this vacuum We aim to present classifications & established techniques that will help practitioners formulate robust credit risk stress tests <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsstrtstcrdtprtflriskmar201232212v20nomacr-13324573083183-phpapp01-120322180326-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Modern credit risk modeling (e.g., Merton, 1974) increasingly relies on advanced mathematical, statistical and numerical echniques to measure and manage risk in redit portfolios This gives rise to model risk (OCC 2011-16) and the possibility of nderstating nherent dangers stemming from very rare yet plausible occurrencs perhaps not in our eference data-sets International supervisors have recognized the importance of stress testing credit risk in the Basel framework (BCBS, 2009) It can and has been argued that the art and science of stress testing has lagged in the domain of credit, vs. other types of risk (e.g., market), and our objective is to help fill this vacuum We aim to present classifications &amp; established techniques that will help practitioners formulate robust credit risk stress tests

Jacobs Str Tst Crdt Prtfl Risk Mar2012 3 22 12 V20 Nomacr from Michael Jacobs, Jr.

]]> 1271 7 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsstrtstcrdtprtflriskmar201232212v20nomacr-13324573083183-phpapp01-120322180326-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11 /slideshow/jacobs-mdl-rsk-par-crdt-der-risk-nov2011-v17-11-7-11/11820833 jacobsmdlrskparcrdtderrisknov2011v1711711-13306280984622-phpapp01-120301130225-phpapp01
It is not difficult to find situations of marked change in variables and with unpredictable event risk implies estimation problems. E.g., Credit spreads in 2008 rise to levels that could never have been forecast based upon previous history. The subprime crisis of 2007/8: credit spreads & volatility rise to unseen levels & shift in debtor behavior (delinquency patterns) E.g., estimating the volatility from data in a calm (turbulent) period implies under (over) estimation of future realized volatility]]>
It is not difficult to find situations of marked change in variables and with unpredictable event risk implies estimation problems. E.g., Credit spreads in 2008 rise to levels that could never have been forecast based upon previous history. The subprime crisis of 2007/8: credit spreads & volatility rise to unseen levels & shift in debtor behavior (delinquency patterns) E.g., estimating the volatility from data in a calm (turbulent) period implies under (over) estimation of future realized volatility]]> Thu, 01 Mar 2012 13:02:18 GMT /slideshow/jacobs-mdl-rsk-par-crdt-der-risk-nov2011-v17-11-7-11/11820833 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11 MikeJacobs It is not difficult to find situations of marked change in variables and with unpredictable event risk implies estimation problems. E.g., Credit spreads in 2008 rise to levels that could never have been forecast based upon previous history. The subprime crisis of 2007/8: credit spreads & volatility rise to unseen levels & shift in debtor behavior (delinquency patterns) E.g., estimating the volatility from data in a calm (turbulent) period implies under (over) estimation of future realized volatility <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsmdlrskparcrdtderrisknov2011v1711711-13306280984622-phpapp01-120301130225-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> It is not difficult to find situations of marked change in variables and with unpredictable event risk implies estimation problems. E.g., Credit spreads in 2008 rise to levels that could never have been forecast based upon previous history. The subprime crisis of 2007/8: credit spreads &amp; volatility rise to unseen levels &amp; shift in debtor behavior (delinquency patterns) E.g., estimating the volatility from data in a calm (turbulent) period implies under (over) estimation of future realized volatility

Jacobs Mdl Rsk Par Crdt Der Risk Nov2011 V17 11 7 11 from Michael Jacobs, Jr.

]]> 774 4 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsmdlrskparcrdtderrisknov2011v1711711-13306280984622-phpapp01-120301130225-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Liquidty Risk Garp 2 16 12 /slideshow/jacobs-liquidty-risk-garp-2-16-12/11820722 jacobsliquidtyriskgarp21612-13306277508036-phpapp01-120301125217-phpapp01
This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.]]>
This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.]]> Thu, 01 Mar 2012 12:50:33 GMT /slideshow/jacobs-liquidty-risk-garp-2-16-12/11820722 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Liquidty Risk Garp 2 16 12 MikeJacobs This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsliquidtyriskgarp21612-13306277508036-phpapp01-120301125217-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.

Jacobs Liquidty Risk Garp 2 16 12 from Michael Jacobs, Jr.

]]> 665 6 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsliquidtyriskgarp21612-13306277508036-phpapp01-120301125217-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Dofdd Frank&Basel3 Risk Nov11 11 8 11 V1 /slideshow/jacobs-dofdd-frankampbasel3-risk-nov11-11-8-11-v1/11194725 jacobsdofddfrankbasel3risknov1111811v1-13271851298407-phpapp01-120121163515-phpapp01
odd-Frank and Basel III Post-Financial Crisis Developments and New Expectations in Regulatory Capital. Following the recent global financial crisis of 2009, financial regulators have responded with arrays of proposals to revise existing risk frameworks for financial institutions with the objective to further strengthen and improve upon bank models. In this meeting, Dr. Michael Jacobs will discuss new developments and expectations in regulatory capital with particular reference to the definition of the capital base, counterparty credit risk, procyclicality of capital, liquidity risk management, and sound compensation practices. He will also explain the implications of the Frank-Dodd rule for financial institutions and will conclude by presenting the implementation schedule for Basel III.]]>
odd-Frank and Basel III Post-Financial Crisis Developments and New Expectations in Regulatory Capital. Following the recent global financial crisis of 2009, financial regulators have responded with arrays of proposals to revise existing risk frameworks for financial institutions with the objective to further strengthen and improve upon bank models. In this meeting, Dr. Michael Jacobs will discuss new developments and expectations in regulatory capital with particular reference to the definition of the capital base, counterparty credit risk, procyclicality of capital, liquidity risk management, and sound compensation practices. He will also explain the implications of the Frank-Dodd rule for financial institutions and will conclude by presenting the implementation schedule for Basel III.]]> Sat, 21 Jan 2012 16:33:42 GMT /slideshow/jacobs-dofdd-frankampbasel3-risk-nov11-11-8-11-v1/11194725 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Dofdd Frank&Basel3 Risk Nov11 11 8 11 V1 MikeJacobs odd-Frank and Basel III Post-Financial Crisis Developments and New Expectations in Regulatory Capital. Following the recent global financial crisis of 2009, financial regulators have responded with arrays of proposals to revise existing risk frameworks for financial institutions with the objective to further strengthen and improve upon bank models. In this meeting, Dr. Michael Jacobs will discuss new developments and expectations in regulatory capital with particular reference to the definition of the capital base, counterparty credit risk, procyclicality of capital, liquidity risk management, and sound compensation practices. He will also explain the implications of the Frank-Dodd rule for financial institutions and will conclude by presenting the implementation schedule for Basel III. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobsdofddfrankbasel3risknov1111811v1-13271851298407-phpapp01-120121163515-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> odd-Frank and Basel III Post-Financial Crisis Developments and New Expectations in Regulatory Capital. Following the recent global financial crisis of 2009, financial regulators have responded with arrays of proposals to revise existing risk frameworks for financial institutions with the objective to further strengthen and improve upon bank models. In this meeting, Dr. Michael Jacobs will discuss new developments and expectations in regulatory capital with particular reference to the definition of the capital base, counterparty credit risk, procyclicality of capital, liquidity risk management, and sound compensation practices. He will also explain the implications of the Frank-Dodd rule for financial institutions and will conclude by presenting the implementation schedule for Basel III.

Jacobs Dofdd Frank&Basel3 Risk Nov11 11 8 11 V1 from Michael Jacobs, Jr.

]]> 470 6 https://cdn.slidesharecdn.com/ss_thumbnails/jacobsdofddfrankbasel3risknov1111811v1-13271851298407-phpapp01-120121163515-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Lgd Risk Resolved Bog And Occ /slideshow/lgd-risk-resolved-bog-and-occ/6241027 lgdriskresolvedbogandocc-12927760917516-phpapp01
This study provides a practical way to anticipate systematic LGD risk. It introduces an LGD function that requires no parameters other than PD, expected LGD, and correlation. This function survives testing against more-elaborate models of corporate credit loss that allow either greater or less LGD risk. Unless a significant improvement were discovered, the LGD function presented here can be used to anticipate systematic LGD risk within a credit loss model or to quantify downturn LGD.]]>
This study provides a practical way to anticipate systematic LGD risk. It introduces an LGD function that requires no parameters other than PD, expected LGD, and correlation. This function survives testing against more-elaborate models of corporate credit loss that allow either greater or less LGD risk. Unless a significant improvement were discovered, the LGD function presented here can be used to anticipate systematic LGD risk within a credit loss model or to quantify downturn LGD.]]> Sun, 19 Dec 2010 10:29:32 GMT /slideshow/lgd-risk-resolved-bog-and-occ/6241027 MikeJacobs@slideshare.net(MikeJacobs) Lgd Risk Resolved Bog And Occ MikeJacobs This study provides a practical way to anticipate systematic LGD risk. It introduces an LGD function that requires no parameters other than PD, expected LGD, and correlation. This function survives testing against more-elaborate models of corporate credit loss that allow either greater or less LGD risk. Unless a significant improvement were discovered, the LGD function presented here can be used to anticipate systematic LGD risk within a credit loss model or to quantify downturn LGD. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lgdriskresolvedbogandocc-12927760917516-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This study provides a practical way to anticipate systematic LGD risk. It introduces an LGD function that requires no parameters other than PD, expected LGD, and correlation. This function survives testing against more-elaborate models of corporate credit loss that allow either greater or less LGD risk. Unless a significant improvement were discovered, the LGD function presented here can be used to anticipate systematic LGD risk within a credit loss model or to quantify downturn LGD.

Lgd Risk Resolved Bog And Occ from Michael Jacobs, Jr.

]]> 500 1 https://cdn.slidesharecdn.com/ss_thumbnails/lgdriskresolvedbogandocc-12927760917516-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Lgd Model Jacobs 10 10 V2[1] /slideshow/lgd-model-jacobs-10-10-v21/5461394 lgdmodeljacobs1010v21-12872530142448-phpapp02
Structural model for loss-given-default, derive compound option formulae , and calibrate to moody\'s defaulted bonds and loans.]]>
Structural model for loss-given-default, derive compound option formulae , and calibrate to moody\'s defaulted bonds and loans.]]> Sat, 16 Oct 2010 13:18:30 GMT /slideshow/lgd-model-jacobs-10-10-v21/5461394 MikeJacobs@slideshare.net(MikeJacobs) Lgd Model Jacobs 10 10 V2[1] MikeJacobs Structural model for loss-given-default, derive compound option formulae , and calibrate to moody\'s defaulted bonds and loans. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lgdmodeljacobs1010v21-12872530142448-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Structural model for loss-given-default, derive compound option formulae , and calibrate to moody\'s defaulted bonds and loans.

Lgd Model Jacobs 10 10 V2[1] from Michael Jacobs, Jr.

]]> 489 1 https://cdn.slidesharecdn.com/ss_thumbnails/lgdmodeljacobs1010v21-12872530142448-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Bag Jacobs Ead Model Ccl Irmc 6 10 /slideshow/bag-jacobs-ead-model-ccl-irmc-6-10/4726784 bagjacobseadmodelcclirmc610-12787843152973-phpapp02
In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such.]]>
In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such.]]> Sat, 10 Jul 2010 12:53:54 GMT /slideshow/bag-jacobs-ead-model-ccl-irmc-6-10/4726784 MikeJacobs@slideshare.net(MikeJacobs) Bag Jacobs Ead Model Ccl Irmc 6 10 MikeJacobs In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bagjacobseadmodelcclirmc610-12787843152973-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such.

Bag Jacobs Ead Model Ccl Irmc 6 10 from Michael Jacobs, Jr.

]]> 941 3 https://cdn.slidesharecdn.com/ss_thumbnails/bagjacobseadmodelcclirmc610-12787843152973-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Jacobs Kiefer Bayes Guide 3 10 V1 /slideshow/jacobs-kiefer-bayes-guide-3-10-v1/3429852 jacobskieferbayesguide310v1-12685942121476-phpapp02
]]>
]]> Sun, 14 Mar 2010 14:17:01 GMT /slideshow/jacobs-kiefer-bayes-guide-3-10-v1/3429852 MikeJacobs@slideshare.net(MikeJacobs) Jacobs Kiefer Bayes Guide 3 10 V1 MikeJacobs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jacobskieferbayesguide310v1-12685942121476-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Jacobs Kiefer Bayes Guide 3 10 V1 from Michael Jacobs, Jr.

]]> 475 5 https://cdn.slidesharecdn.com/ss_thumbnails/jacobskieferbayesguide310v1-12685942121476-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Val Econ Cap Mdls Risk Conf Jacobs 1 10 V1 /MikeJacobs/val-econ-cap-mdls-risk-conf-jacobs-1-10-v1 valeconcapmdlsriskconfjacobs110v1-12648625183981-phpapp02
]]>
]]> Sat, 30 Jan 2010 08:42:06 GMT /MikeJacobs/val-econ-cap-mdls-risk-conf-jacobs-1-10-v1 MikeJacobs@slideshare.net(MikeJacobs) Val Econ Cap Mdls Risk Conf Jacobs 1 10 V1 MikeJacobs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/valeconcapmdlsriskconfjacobs110v1-12648625183981-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Val Econ Cap Mdls Risk Conf Jacobs 1 10 V1 from Michael Jacobs, Jr.

]]> 448 2 https://cdn.slidesharecdn.com/ss_thumbnails/valeconcapmdlsriskconfjacobs110v1-12648625183981-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Risk Aggregation Inanoglu Jacobs 6 09 V1 /MikeJacobs/riskaggregationinanoglujacobs609v1 riskaggregationinanoglujacobs609v1-124627899908-phpapp01
]]>
]]> Mon, 29 Jun 2009 07:36:57 GMT /MikeJacobs/riskaggregationinanoglujacobs609v1 MikeJacobs@slideshare.net(MikeJacobs) Risk Aggregation Inanoglu Jacobs 6 09 V1 MikeJacobs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/riskaggregationinanoglujacobs609v1-124627899908-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Risk Aggregation Inanoglu Jacobs 6 09 V1 from Michael Jacobs, Jr.

]]> 683 4 https://cdn.slidesharecdn.com/ss_thumbnails/riskaggregationinanoglujacobs609v1-124627899908-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Understanding and Predicting Ultimate Loss-Given-Default on Bonds and Loans /slideshow/LGDPublicationPresentation2007FMA/1079143 LGDPublicationPresentation2007FMA-123575912746-phpapp02
]]>
]]> Fri, 27 Feb 2009 12:26:02 GMT /slideshow/LGDPublicationPresentation2007FMA/1079143 MikeJacobs@slideshare.net(MikeJacobs) Understanding and Predicting Ultimate Loss-Given-Default on Bonds and Loans MikeJacobs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/LGDPublicationPresentation2007FMA-123575912746-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Understanding and Predicting Ultimate Loss-Given-Default on Bonds and Loans from Michael Jacobs, Jr.

]]> 1833 10 https://cdn.slidesharecdn.com/ss_thumbnails/LGDPublicationPresentation2007FMA-123575912746-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 An Empirical Study of Exposure at Default /slideshow/AnEmpiricalStudyOfEADJacobs1208V4/1079130 AnEmpiricalStudyOfEADJacobs1208V4-12357589941-phpapp01
]]>
]]> Fri, 27 Feb 2009 12:23:21 GMT /slideshow/AnEmpiricalStudyOfEADJacobs1208V4/1079130 MikeJacobs@slideshare.net(MikeJacobs) An Empirical Study of Exposure at Default MikeJacobs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/AnEmpiricalStudyOfEADJacobs1208V4-12357589941-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

An Empirical Study of Exposure at Default from Michael Jacobs, Jr.

]]> 1873 6 https://cdn.slidesharecdn.com/ss_thumbnails/AnEmpiricalStudyOfEADJacobs1208V4-12357589941-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 An Empirical Study of the Returns on Defaulted Debt and the Discount Rate for Loss-Given-Default /slideshow/RDDDiscRtLGDJacobsPres1208/1079125 RDDDiscRtLGDJacobsPres1208-123575884163-phpapp02
]]>
]]> Fri, 27 Feb 2009 12:22:01 GMT /slideshow/RDDDiscRtLGDJacobsPres1208/1079125 MikeJacobs@slideshare.net(MikeJacobs) An Empirical Study of the Returns on Defaulted Debt and the Discount Rate for Loss-Given-Default MikeJacobs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/RDDDiscRtLGDJacobsPres1208-123575884163-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

An Empirical Study of the Returns on Defaulted Debt and the Discount Rate for Loss-Given-Default from Michael Jacobs, Jr.

]]> 1733 7 https://cdn.slidesharecdn.com/ss_thumbnails/RDDDiscRtLGDJacobsPres1208-123575884163-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-MikeJacobs-48x48.jpg?cb=1611363032 Researcher and practitioner in economics and finance for 20 years. Recently interested in all aspects of risk management - stress testing, model risk, credit risk, risk aggregation, economic capital, model development and validation, prudential regulation (CCAR, Basel capital accord), markets (distressed debt), derivatives (structured products), financial distress and bankruptcy. Previously did work on RAROC and capital modelling, term structure and futures markets. Began career in quantitative equity research. Specialties: Credit Risk, Corprorate Credit Modeling, Distressed Debt, Basel II, Quanatitative Analysis, Econometric Methods, Computational Finance, Bayesian Methods. http://www.michaeljacobsjr.com https://cdn.slidesharecdn.com/ss_thumbnails/jacobsstresstestingaug138-15-13v4-130815065707-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/jacobs-stress-testingaug1381513v4/25270170 Jacobs stress testing_... https://cdn.slidesharecdn.com/ss_thumbnails/jacobsregfrmwrksmktriskpresentationjulu1271512v5-13440980334423-phpapp02-120804113454-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/jacobs-reg-frmwrks-mkt-risk-presentation-julu12-7-15-12-v5/13871850 Jacobs Reg Frmwrks Mkt... https://cdn.slidesharecdn.com/ss_thumbnails/jacobsdoddfrankbasel3july1271512v16-13440978554917-phpapp01-120804113247-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/jacobs-dodd-frankampbasel3-july12-7-15-12-v16/13871830 Jacobs Dodd Frank&amp;...