�ݺ�ߣshows by User: yamanote

�ݺ�ߣshows by User: yamanote / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: yamanote / Tue, 27 Mar 2012 22:21:13 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: yamanote Using Cross Asset Information To Improve Portfolio Risk Estimation /slideshow/using-cross-asset-information-to-improve-portfolio-risk-estimation/12185230 usingcrossassetinformationtoimproveportfolioriskestimationwb-133290463756-phpapp02-120327223946-phpapp02
There are obvious relationships between the various securities of a given firm that impact our expectations of risk. For example, if fixed income investors expect a corporate bond of a company to default, there must be a related bankruptcy event that would negatively impact shareholders in that firm. In this presentation, Nick will describe how to use data from bond and option markets to improve risk estimation for equity portfolios, and how to use information from the equity markets to improve estimation of credit risk in fixed income securities. The goal of the process is to create holistic risk estimation where all expectations of risk are mutually consistent across the entire capital structure of a firm, and related derivatives.]]>
There are obvious relationships between the various securities of a given firm that impact our expectations of risk. For example, if fixed income investors expect a corporate bond of a company to default, there must be a related bankruptcy event that would negatively impact shareholders in that firm. In this presentation, Nick will describe how to use data from bond and option markets to improve risk estimation for equity portfolios, and how to use information from the equity markets to improve estimation of credit risk in fixed income securities. The goal of the process is to create holistic risk estimation where all expectations of risk are mutually consistent across the entire capital structure of a firm, and related derivatives.]]> Tue, 27 Mar 2012 22:21:13 GMT /slideshow/using-cross-asset-information-to-improve-portfolio-risk-estimation/12185230 yamanote@slideshare.net(yamanote) Using Cross Asset Information To Improve Portfolio Risk Estimation yamanote There are obvious relationships between the various securities of a given firm that impact our expectations of risk. For example, if fixed income investors expect a corporate bond of a company to default, there must be a related bankruptcy event that would negatively impact shareholders in that firm. In this presentation, Nick will describe how to use data from bond and option markets to improve risk estimation for equity portfolios, and how to use information from the equity markets to improve estimation of credit risk in fixed income securities. The goal of the process is to create holistic risk estimation where all expectations of risk are mutually consistent across the entire capital structure of a firm, and related derivatives. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/usingcrossassetinformationtoimproveportfolioriskestimationwb-133290463756-phpapp02-120327223946-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> There are obvious relationships between the various securities of a given firm that impact our expectations of risk. For example, if fixed income investors expect a corporate bond of a company to default, there must be a related bankruptcy event that would negatively impact shareholders in that firm. In this presentation, Nick will describe how to use data from bond and option markets to improve risk estimation for equity portfolios, and how to use information from the equity markets to improve estimation of credit risk in fixed income securities. The goal of the process is to create holistic risk estimation where all expectations of risk are mutually consistent across the entire capital structure of a firm, and related derivatives.

Using Cross Asset Information To Improve Portfolio Risk Estimation from yamanote

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0 The Search for a Better Risk Model - MPT Forum Tokyo March 1st 2012 /slideshow/the-search-for-a-better-risk-model-mpt-forum-tokyo-march-1st-2012/12185176 mptforummarch1st2012-13329040729835-phpapp02-120327221341-phpapp02
This presentation discusses the three most common ways to estimate a multi-factor risk model, sheds some light on the numerous assumptions underlying the models, and provides some thoughts about how to address those assumptions to make the models better fit the real world. The Northfield hybrid risk model is discussed. Non-stationary volatility, correlation, clusters in volatility, the use of forward-looking signals such as implied-volatility and cross-sectional dispersion, as well as the use of quantified news information to update risk forecasts are included.]]>
This presentation discusses the three most common ways to estimate a multi-factor risk model, sheds some light on the numerous assumptions underlying the models, and provides some thoughts about how to address those assumptions to make the models better fit the real world. The Northfield hybrid risk model is discussed. Non-stationary volatility, correlation, clusters in volatility, the use of forward-looking signals such as implied-volatility and cross-sectional dispersion, as well as the use of quantified news information to update risk forecasts are included.]]> Tue, 27 Mar 2012 22:12:52 GMT /slideshow/the-search-for-a-better-risk-model-mpt-forum-tokyo-march-1st-2012/12185176 yamanote@slideshare.net(yamanote) The Search for a Better Risk Model - MPT Forum Tokyo March 1st 2012 yamanote This presentation discusses the three most common ways to estimate a multi-factor risk model, sheds some light on the numerous assumptions underlying the models, and provides some thoughts about how to address those assumptions to make the models better fit the real world. The Northfield hybrid risk model is discussed. Non-stationary volatility, correlation, clusters in volatility, the use of forward-looking signals such as implied-volatility and cross-sectional dispersion, as well as the use of quantified news information to update risk forecasts are included. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mptforummarch1st2012-13329040729835-phpapp02-120327221341-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation discusses the three most common ways to estimate a multi-factor risk model, sheds some light on the numerous assumptions underlying the models, and provides some thoughts about how to address those assumptions to make the models better fit the real world. The Northfield hybrid risk model is discussed. Non-stationary volatility, correlation, clusters in volatility, the use of forward-looking signals such as implied-volatility and cross-sectional dispersion, as well as the use of quantified news information to update risk forecasts are included.

The Search for a Better Risk Model - MPT Forum Tokyo March 1st 2012 from yamanote

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0 The X Factor /slideshow/the-x-factor/10162884 ppt1wadethexfactor2011-1321327577357-phpapp01-111114213049-phpapp01
Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years. We review fundamental, macro-economic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” least-squares regression, artificial immune systems, single-pass clustering, semantic clustering, social network influence measurement, layer-embedded networks, block-modeling, and more.]]>
Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years. We review fundamental, macro-economic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” least-squares regression, artificial immune systems, single-pass clustering, semantic clustering, social network influence measurement, layer-embedded networks, block-modeling, and more.]]> Mon, 14 Nov 2011 21:29:12 GMT /slideshow/the-x-factor/10162884 yamanote@slideshare.net(yamanote) The X Factor yamanote Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years. We review fundamental, macro-economic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” least-squares regression, artificial immune systems, single-pass clustering, semantic clustering, social network influence measurement, layer-embedded networks, block-modeling, and more. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ppt1wadethexfactor2011-1321327577357-phpapp01-111114213049-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years. We review fundamental, macro-economic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” least-squares regression, artificial immune systems, single-pass clustering, semantic clustering, social network influence measurement, layer-embedded networks, block-modeling, and more.

The X Factor from yamanote

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0 Nick Wade Using A Structural Model For Enterprise Risk, Dst Conference 2011 04 12 /slideshow/nick-wade-using-a-structural-model-for-enterprise-risk-dst-conference-2011-04-12/7692373 nickwadeusingastructuralmodelforenterpriseriskdstconference20110412-13033682924074-phpapp01
On why a multi-factor or structural model of risk might be a good idea at the enterprise level, rather than the more common VaR models based simply on historical returns]]>
On why a multi-factor or structural model of risk might be a good idea at the enterprise level, rather than the more common VaR models based simply on historical returns]]> Thu, 21 Apr 2011 01:47:48 GMT /slideshow/nick-wade-using-a-structural-model-for-enterprise-risk-dst-conference-2011-04-12/7692373 yamanote@slideshare.net(yamanote) Nick Wade Using A Structural Model For Enterprise Risk, Dst Conference 2011 04 12 yamanote On why a multi-factor or structural model of risk might be a good idea at the enterprise level, rather than the more common VaR models based simply on historical returns <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nickwadeusingastructuralmodelforenterpriseriskdstconference20110412-13033682924074-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> On why a multi-factor or structural model of risk might be a good idea at the enterprise level, rather than the more common VaR models based simply on historical returns

Nick Wade Using A Structural Model For Enterprise Risk, Dst Conference 2011 04 12 from yamanote

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0 Agent Based Models 2010 /slideshow/agent-based-models-2010/6057542 agentbasedmodels-12917074304613-phpapp02
A brief literature review and roadmap through agent-based models of financial markets. Laying out the key decisions agent based model builders need to make and some of the empirical results from recent models investigating the effect of short-selling bans, leverage etc.]]>
A brief literature review and roadmap through agent-based models of financial markets. Laying out the key decisions agent based model builders need to make and some of the empirical results from recent models investigating the effect of short-selling bans, leverage etc.]]> Tue, 07 Dec 2010 01:38:19 GMT /slideshow/agent-based-models-2010/6057542 yamanote@slideshare.net(yamanote) Agent Based Models 2010 yamanote A brief literature review and roadmap through agent-based models of financial markets. Laying out the key decisions agent based model builders need to make and some of the empirical results from recent models investigating the effect of short-selling bans, leverage etc. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agentbasedmodels-12917074304613-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A brief literature review and roadmap through agent-based models of financial markets. Laying out the key decisions agent based model builders need to make and some of the empirical results from recent models investigating the effect of short-selling bans, leverage etc.

Agent Based Models 2010 from yamanote

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0 Balancing quantitative models with common sense 2008 /slideshow/balancing-quantitative-models-with-common-sense-2008/6057534 macquariequantconferencesingapore82008-12917073142167-phpapp01
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]]> Tue, 07 Dec 2010 01:36:51 GMT /slideshow/balancing-quantitative-models-with-common-sense-2008/6057534 yamanote@slideshare.net(yamanote) Balancing quantitative models with common sense 2008 yamanote <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/macquariequantconferencesingapore82008-12917073142167-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Balancing quantitative models with common sense 2008 from yamanote

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0 Intra Horizon Risk 2010 /slideshow/ntra-horizon-risk-2010/6057524 nwintrahorizonriskformated-12917071889434-phpapp02
Looking at the risk within an investment period rather than just at the end of the period. Evaluating the effect on VaR of non-Normal distributions, jumps, and drift]]>
Looking at the risk within an investment period rather than just at the end of the period. Evaluating the effect on VaR of non-Normal distributions, jumps, and drift]]> Tue, 07 Dec 2010 01:34:42 GMT /slideshow/ntra-horizon-risk-2010/6057524 yamanote@slideshare.net(yamanote) Intra Horizon Risk 2010 yamanote Looking at the risk within an investment period rather than just at the end of the period. Evaluating the effect on VaR of non-Normal distributions, jumps, and drift <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nwintrahorizonriskformated-12917071889434-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Looking at the risk within an investment period rather than just at the end of the period. Evaluating the effect on VaR of non-Normal distributions, jumps, and drift

Intra Horizon Risk 2010 from yamanote

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0 Conditional Correlation 2009 /slideshow/conditional-correlation-2009/6057514 conditionalcorrelationnw20090827-12917071080486-phpapp01
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]]> Tue, 07 Dec 2010 01:32:48 GMT /slideshow/conditional-correlation-2009/6057514 yamanote@slideshare.net(yamanote) Conditional Correlation 2009 yamanote <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/conditionalcorrelationnw20090827-12917071080486-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Conditional Correlation 2009 from yamanote

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0 Risk Model Methodologies /slideshow/risk-model-methodologies/6057486 1nickriskmodelmethodologies20050928-12917068882084-phpapp02
A review of the assumptions behind fundamental, macro, and statistical risk models. Pros and cons of each approach. Introducing adaptive hybrid risk models.]]>
A review of the assumptions behind fundamental, macro, and statistical risk models. Pros and cons of each approach. Introducing adaptive hybrid risk models.]]> Tue, 07 Dec 2010 01:30:36 GMT /slideshow/risk-model-methodologies/6057486 yamanote@slideshare.net(yamanote) Risk Model Methodologies yamanote A review of the assumptions behind fundamental, macro, and statistical risk models. Pros and cons of each approach. Introducing adaptive hybrid risk models. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1nickriskmodelmethodologies20050928-12917068882084-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A review of the assumptions behind fundamental, macro, and statistical risk models. Pros and cons of each approach. Introducing adaptive hybrid risk models.

Risk Model Methodologies from yamanote

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/usingcrossassetinformationtoimproveportfolioriskestimationwb-133290463756-phpapp02-120327223946-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/using-cross-asset-information-to-improve-portfolio-risk-estimation/12185230 Using Cross Asset Info... https://cdn.slidesharecdn.com/ss_thumbnails/mptforummarch1st2012-13329040729835-phpapp02-120327221341-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-search-for-a-better-risk-model-mpt-forum-tokyo-march-1st-2012/12185176 The Search for a Bette... https://cdn.slidesharecdn.com/ss_thumbnails/ppt1wadethexfactor2011-1321327577357-phpapp01-111114213049-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-x-factor/10162884 The X Factor