�ݺ�ߣshows by User: Volatility

�ݺ�ߣshows by User: Volatility / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: Volatility / Sat, 30 Aug 2014 03:11:46 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: Volatility Quantitative Methods for Counterparty Risk /slideshow/quantitative-methods-for-counterparty-risk/38510430 artursepphelsinkipresentation2009-140830031146-phpapp01
1) Counterparty default risk 2) Modelling aspects 3) Pricing of credit instruments 4) Analytical Methods 5) FFT based methods 6) PDE based methods 7) Illustrations]]>
1) Counterparty default risk 2) Modelling aspects 3) Pricing of credit instruments 4) Analytical Methods 5) FFT based methods 6) PDE based methods 7) Illustrations]]> Sat, 30 Aug 2014 03:11:46 GMT /slideshow/quantitative-methods-for-counterparty-risk/38510430 Volatility@slideshare.net(Volatility) Quantitative Methods for Counterparty Risk Volatility 1) Counterparty default risk 2) Modelling aspects 3) Pricing of credit instruments 4) Analytical Methods 5) FFT based methods 6) PDE based methods 7) Illustrations <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/artursepphelsinkipresentation2009-140830031146-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Counterparty default risk 2) Modelling aspects 3) Pricing of credit instruments 4) Analytical Methods 5) FFT based methods 6) PDE based methods 7) Illustrations

Quantitative Methods for Counterparty Risk from Volatility

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0 Realized and implied index skews, jumps, and the failure of the minimum-variance hedging /slideshow/realized-and-implied-index-skews-jumps-and-the-failure-of-the-minimumvariance-hedging/38509950 arturseppglobalderivatives2014-140830023229-phpapp01
1) Empirical evidence for the log-normality of implied and realized volatilities of stock indices 2) Apply the beta stochastic volatility (SV) model for quantifying implied and realized index skews 3) Origin of the premium for risk-neutral skews and its impacts on profit-and-loss (P\&L) of delta-hedging strategies 4) Closed-form solution for the mean-reverting log-normal beta SV model 5) Optimal delta-hedging strategies to improve Sharpe ratios 6) Argue why log-normal beta SV model is better than its alternatives ]]>
1) Empirical evidence for the log-normality of implied and realized volatilities of stock indices 2) Apply the beta stochastic volatility (SV) model for quantifying implied and realized index skews 3) Origin of the premium for risk-neutral skews and its impacts on profit-and-loss (P\&L) of delta-hedging strategies 4) Closed-form solution for the mean-reverting log-normal beta SV model 5) Optimal delta-hedging strategies to improve Sharpe ratios 6) Argue why log-normal beta SV model is better than its alternatives ]]> Sat, 30 Aug 2014 02:32:29 GMT /slideshow/realized-and-implied-index-skews-jumps-and-the-failure-of-the-minimumvariance-hedging/38509950 Volatility@slideshare.net(Volatility) Realized and implied index skews, jumps, and the failure of the minimum-variance hedging Volatility 1) Empirical evidence for the log-normality of implied and realized volatilities of stock indices 2) Apply the beta stochastic volatility (SV) model for quantifying implied and realized index skews 3) Origin of the premium for risk-neutral skews and its impacts on profit-and-loss (P\&L) of delta-hedging strategies 4) Closed-form solution for the mean-reverting log-normal beta SV model 5) Optimal delta-hedging strategies to improve Sharpe ratios 6) Argue why log-normal beta SV model is better than its alternatives <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/arturseppglobalderivatives2014-140830023229-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Empirical evidence for the log-normality of implied and realized volatilities of stock indices 2) Apply the beta stochastic volatility (SV) model for quantifying implied and realized index skews 3) Origin of the premium for risk-neutral skews and its impacts on profit-and-loss (P\&L) of delta-hedging strategies 4) Closed-form solution for the mean-reverting log-normal beta SV model 5) Optimal delta-hedging strategies to improve Sharpe ratios 6) Argue why log-normal beta SV model is better than its alternatives

Realized and implied index skews, jumps, and the failure of the minimum-variance hedging from Volatility

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0 Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable Effective Hedging /slideshow/consistently-modeling-joint-dynamics-of-volatility-and-underlying-to-enable-effective-hedging/38509861 presentationglobalderivatives2013-140830022536-phpapp02
1) Analyze the dependence between returns and volatility in conventional stochastic volatility (SV) models 2) Introduce the beta SV model by Karasinski-Sepp, "Beta Stochastic Volatility Model", Risk, October 2012 3) Illustrate intuitive and robust calibration of the beta SV model to historical and implied data 4) Mix local and stochastic volatility in the beta SV model to produce different volatility regimes and equity delta]]>
1) Analyze the dependence between returns and volatility in conventional stochastic volatility (SV) models 2) Introduce the beta SV model by Karasinski-Sepp, "Beta Stochastic Volatility Model", Risk, October 2012 3) Illustrate intuitive and robust calibration of the beta SV model to historical and implied data 4) Mix local and stochastic volatility in the beta SV model to produce different volatility regimes and equity delta]]> Sat, 30 Aug 2014 02:25:36 GMT /slideshow/consistently-modeling-joint-dynamics-of-volatility-and-underlying-to-enable-effective-hedging/38509861 Volatility@slideshare.net(Volatility) Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable Effective Hedging Volatility 1) Analyze the dependence between returns and volatility in conventional stochastic volatility (SV) models 2) Introduce the beta SV model by Karasinski-Sepp, "Beta Stochastic Volatility Model", Risk, October 2012 3) Illustrate intuitive and robust calibration of the beta SV model to historical and implied data 4) Mix local and stochastic volatility in the beta SV model to produce different volatility regimes and equity delta <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationglobalderivatives2013-140830022536-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Analyze the dependence between returns and volatility in conventional stochastic volatility (SV) models 2) Introduce the beta SV model by Karasinski-Sepp, "Beta Stochastic Volatility Model", Risk, October 2012 3) Illustrate intuitive and robust calibration of the beta SV model to historical and implied data 4) Mix local and stochastic volatility in the beta SV model to produce different volatility regimes and equity delta

Consistently Modeling Joint Dynamics of Volatility and Underlying To Enable Effective Hedging from Volatility

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0 Achieving Consistent Modeling Of VIX and Equities Derivatives /slideshow/achieving-consistent-modeling-of-vix-and-equities-derivatives/38509753 globalderivatives2012-140830021743-phpapp02
1) Discuss model complexity and calibration 2) Emphasize intuitive and robust calibration of sophisticated volatility models avoiding non-linear calibrations 3) Present local stochastic volatility models with jumps to achieve joint calibration to VIX options and (short-term) S&P500 options 4) Present two factor stochastic volatility model to fit both the short-term and long-term S&P500 option skews]]>
1) Discuss model complexity and calibration 2) Emphasize intuitive and robust calibration of sophisticated volatility models avoiding non-linear calibrations 3) Present local stochastic volatility models with jumps to achieve joint calibration to VIX options and (short-term) S&P500 options 4) Present two factor stochastic volatility model to fit both the short-term and long-term S&P500 option skews]]> Sat, 30 Aug 2014 02:17:43 GMT /slideshow/achieving-consistent-modeling-of-vix-and-equities-derivatives/38509753 Volatility@slideshare.net(Volatility) Achieving Consistent Modeling Of VIX and Equities Derivatives Volatility 1) Discuss model complexity and calibration 2) Emphasize intuitive and robust calibration of sophisticated volatility models avoiding non-linear calibrations 3) Present local stochastic volatility models with jumps to achieve joint calibration to VIX options and (short-term) S&P500 options 4) Present two factor stochastic volatility model to fit both the short-term and long-term S&P500 option skews <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/globalderivatives2012-140830021743-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Discuss model complexity and calibration 2) Emphasize intuitive and robust calibration of sophisticated volatility models avoiding non-linear calibrations 3) Present local stochastic volatility models with jumps to achieve joint calibration to VIX options and (short-term) S&P500 options 4) Present two factor stochastic volatility model to fit both the short-term and long-term S&P500 option skews

Achieving Consistent Modeling Of VIX and Equities Derivatives from Volatility

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0 Efficient Numerical PDE Methods to Solve Calibration and Pricing Problems in Local Stochastic Volatility Models /slideshow/efficient-numerical-pde-methods-to-solve-calibration-and-pricing-problems-in-local-stochastic-volatility-models/38509675 seppglobalderivatives11-140830021221-phpapp01
1) Volatility modelling 2) Local stochastic volatility models: stochastic volatility, jumps, local volatility 3) Calibration of parametric local volatility models using partial differential equation (PDE) methods 4) Calibration of non-parametric local volatility volatility models with jumps and stochastic volatility using PDE methods 5) Numerical methods for PDEs 6) Illustrations using SPX and VIX data]]>
1) Volatility modelling 2) Local stochastic volatility models: stochastic volatility, jumps, local volatility 3) Calibration of parametric local volatility models using partial differential equation (PDE) methods 4) Calibration of non-parametric local volatility volatility models with jumps and stochastic volatility using PDE methods 5) Numerical methods for PDEs 6) Illustrations using SPX and VIX data]]> Sat, 30 Aug 2014 02:12:21 GMT /slideshow/efficient-numerical-pde-methods-to-solve-calibration-and-pricing-problems-in-local-stochastic-volatility-models/38509675 Volatility@slideshare.net(Volatility) Efficient Numerical PDE Methods to Solve Calibration and Pricing Problems in Local Stochastic Volatility Models Volatility 1) Volatility modelling 2) Local stochastic volatility models: stochastic volatility, jumps, local volatility 3) Calibration of parametric local volatility models using partial differential equation (PDE) methods 4) Calibration of non-parametric local volatility volatility models with jumps and stochastic volatility using PDE methods 5) Numerical methods for PDEs 6) Illustrations using SPX and VIX data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/seppglobalderivatives11-140830021221-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Volatility modelling 2) Local stochastic volatility models: stochastic volatility, jumps, local volatility 3) Calibration of parametric local volatility models using partial differential equation (PDE) methods 4) Calibration of non-parametric local volatility volatility models with jumps and stochastic volatility using PDE methods 5) Numerical methods for PDEs 6) Illustrations using SPX and VIX data

Efficient Numerical PDE Methods to Solve Calibration and Pricing Problems in Local Stochastic Volatility Models from Volatility

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0 Stochastic Local Volatility Models: Theory and Implementation /slideshow/seppstochasticlocalvolatility/38509428 seppstochasticlocalvolatility-140830015509-phpapp01
1) Hedging and volatility 2) Review of volatility models 3) Local volatility models with jumps and stochastic volatility 4) Calibration using Kolmogorov equations 5) PDE based methods in one dimension 5) PDE based methods in two dimensions 7) Illustrations]]>
1) Hedging and volatility 2) Review of volatility models 3) Local volatility models with jumps and stochastic volatility 4) Calibration using Kolmogorov equations 5) PDE based methods in one dimension 5) PDE based methods in two dimensions 7) Illustrations]]> Sat, 30 Aug 2014 01:55:09 GMT /slideshow/seppstochasticlocalvolatility/38509428 Volatility@slideshare.net(Volatility) Stochastic Local Volatility Models: Theory and Implementation Volatility 1) Hedging and volatility 2) Review of volatility models 3) Local volatility models with jumps and stochastic volatility 4) Calibration using Kolmogorov equations 5) PDE based methods in one dimension 5) PDE based methods in two dimensions 7) Illustrations <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/seppstochasticlocalvolatility-140830015509-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Hedging and volatility 2) Review of volatility models 3) Local volatility models with jumps and stochastic volatility 4) Calibration using Kolmogorov equations 5) PDE based methods in one dimension 5) PDE based methods in two dimensions 7) Illustrations

Stochastic Local Volatility Models: Theory and Implementation from Volatility

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0 An Approximate Distribution of Delta-Hedging Errors in a Jump-Diffusion Model with Discrete Trading and Transaction Costs /slideshow/an-approximate-distribution-of-deltahedging-errors-in-a-jumpdiffusion-model-with-discrete-trading-and-transaction-costs/38509333 casspresentation-140830014724-phpapp02
1) Analyse the distribution of the profit&loss (P&L) of delta-hedging strategy for vanilla options in Black-Scholes-Merton (BSM) model and an extension of the Merton jump-diffusion (JDM) model assuming discrete trading and transaction costs 2) Examine the connection between the realized variance and the realized P&L 3) Find approximate solutions for the P&L volatility and the expected total transaction costs 4) Apply the mean-variance analysis to find the trade-off between the costs and P&L variance given hedger's risk tolerance 5) Consider hedging strategies to minimize the jump risk]]>
1) Analyse the distribution of the profit&loss (P&L) of delta-hedging strategy for vanilla options in Black-Scholes-Merton (BSM) model and an extension of the Merton jump-diffusion (JDM) model assuming discrete trading and transaction costs 2) Examine the connection between the realized variance and the realized P&L 3) Find approximate solutions for the P&L volatility and the expected total transaction costs 4) Apply the mean-variance analysis to find the trade-off between the costs and P&L variance given hedger's risk tolerance 5) Consider hedging strategies to minimize the jump risk]]> Sat, 30 Aug 2014 01:47:23 GMT /slideshow/an-approximate-distribution-of-deltahedging-errors-in-a-jumpdiffusion-model-with-discrete-trading-and-transaction-costs/38509333 Volatility@slideshare.net(Volatility) An Approximate Distribution of Delta-Hedging Errors in a Jump-Diffusion Model with Discrete Trading and Transaction Costs Volatility 1) Analyse the distribution of the profit&loss (P&L) of delta-hedging strategy for vanilla options in Black-Scholes-Merton (BSM) model and an extension of the Merton jump-diffusion (JDM) model assuming discrete trading and transaction costs 2) Examine the connection between the realized variance and the realized P&L 3) Find approximate solutions for the P&L volatility and the expected total transaction costs 4) Apply the mean-variance analysis to find the trade-off between the costs and P&L variance given hedger's risk tolerance 5) Consider hedging strategies to minimize the jump risk <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/casspresentation-140830014724-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> 1) Analyse the distribution of the profit&loss (P&L) of delta-hedging strategy for vanilla options in Black-Scholes-Merton (BSM) model and an extension of the Merton jump-diffusion (JDM) model assuming discrete trading and transaction costs 2) Examine the connection between the realized variance and the realized P&L 3) Find approximate solutions for the P&L volatility and the expected total transaction costs 4) Apply the mean-variance analysis to find the trade-off between the costs and P&L variance given hedger's risk tolerance 5) Consider hedging strategies to minimize the jump risk

An Approximate Distribution of Delta-Hedging Errors in a Jump-Diffusion Model with Discrete Trading and Transaction Costs from Volatility

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0 Volatility derivatives and default risk /Volatility/quant2007presentation quant2007presentation-140830013556-phpapp01
Volatility, Credit, Affine Models, Jump-to-Default, Variance Swap]]>
Volatility, Credit, Affine Models, Jump-to-Default, Variance Swap]]> Sat, 30 Aug 2014 01:35:56 GMT /Volatility/quant2007presentation Volatility@slideshare.net(Volatility) Volatility derivatives and default risk Volatility Volatility, Credit, Affine Models, Jump-to-Default, Variance Swap <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/quant2007presentation-140830013556-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Volatility, Credit, Affine Models, Jump-to-Default, Variance Swap

Volatility derivatives and default risk from Volatility

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/artursepphelsinkipresentation2009-140830031146-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/quantitative-methods-for-counterparty-risk/38510430 Quantitative Methods f... https://cdn.slidesharecdn.com/ss_thumbnails/arturseppglobalderivatives2014-140830023229-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/realized-and-implied-index-skews-jumps-and-the-failure-of-the-minimumvariance-hedging/38509950 Realized and implied i... https://cdn.slidesharecdn.com/ss_thumbnails/presentationglobalderivatives2013-140830022536-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/consistently-modeling-joint-dynamics-of-volatility-and-underlying-to-enable-effective-hedging/38509861 Consistently Modeling ...