SSRN Author: Antoine SavineAntoine Savine SSRN Content
https://www.ssrn.com/author=1851237
https://www.ssrn.com/rss/en-usTue, 01 Jan 2019 01:06:17 GMTeditor@ssrn.com (Editor)Tue, 01 Jan 2019 01:06:17 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Financial Cash-Flow Scripting: Beyond Valuation (Presentation Slides)My life in scripting, by Jesper Andreasen<br>(A brief history of scripting for financial derivatives) <br><br>Antoine came to General Re Financial Products in London in 1998 with a lot of youthful spirit and many refreshing ideas. One of them was a financial payoff language called SynTech (Syntactic interpreter Technology). I am not sure that I was immediately convinced but when he connected it to a real model and priced some structures that we made up on the fly, I was hooked. I learned SynTech in hours but it took me months to figure out how it was put together. A process that forced me to learn structured programming in general and CPP (c plus plus) in particular. <br><br>As always, it was a general struggle to keep up with the financial innovation, and constant re-coding of new payoffs was a painful and error prone process. We had been toying with a cocktail of Visual Basic for scripting of the payoffs, and scenarios of future prices generated by CPP programs. However, the ...
https://www.ssrn.com/abstract=3281884
https://www.ssrn.com/1751039.htmlMon, 31 Dec 2018 13:56:43 GMTNew: Modern Computational Finance: AAD and Parallel Simulations (Table of Contents and Preface)Public preview of Antoine Savine's book "Modern Computational Finance: AAD and Parallel Simulations", published by Wiley in November 2018.
https://www.ssrn.com/abstract=3281877
https://www.ssrn.com/1746983.htmlThu, 13 Dec 2018 16:41:54 GMTREVISION: LSM Reloaded - Differentiate xVA on your iPad MiniThis article by Brian Huge and Antoine Savine reviews the so called least square methodology (LSM) and its application for the valuation and risk of callable exotics and regulatory value adjustments (xVA). We derive valuation algorithms for xVA, both with or without collateral, that are particularly accurate, efficient and practical. These algorithms are based on a reformulation of xVA, designed by Jesper Andreasen and implemented in Danske Bank's award winning systems, that hasn't been previously published in full. We then investigate the matter of risk sensitivities, in the context of Algorithmic Automated Differentiation (AAD). A rather recent addition to the financial mathematics toolbox, AAD is presently generally acknowledged as a vastly superior alternative to the classical estimation of risk sensitivities through finite differences, and the only practical means for the calculation of the large number of sensitivities in the context of xVA. The theory and implementation of ...
https://www.ssrn.com/abstract=2966155
https://www.ssrn.com/1739428.htmlThu, 15 Nov 2018 20:09:15 GMTREVISION: Theory of VolatilityAntoine Savine on volatility, generalized derivatives, Tanaka's formula and extensions of Dupire's classical results.
Bruno Dupire defined in 1993 an extension of Black & Scholes’s model consistent with the market implied volatility smile on equity derivatives. This paper shows how Dupire’s theory can be extended to take into account more realistic assumptions, such as interest rates, dividends, stochastic volatility and jumps. Moreover, we describe a similar theory in an interest rate setting, deriving a “caplet smile” formula, similar to Dupire’s “local volaitlity” formula, defining an extended Vasicek model consistent with the market implied caplet smile. Our results also lead to an efficient calibration strategy for Markov interest rate models, and they are likely to considerably speed-up the calibration of non Gaussian models, with or without taking into account the volatility smile.
The first section introduces the fundamental mathematical concepts and notations, and ...
https://www.ssrn.com/abstract=3173772
https://www.ssrn.com/1739427.htmlThu, 15 Nov 2018 20:08:13 GMTREVISION: From Model to Market Risks: The Implicit Function Theorem (IFT) DemystifiedOne persisting conundrum in the theory and practice of quantitative risk management models is the relationship of model risks (the risk sensitivities of a transaction or set of transactions to the parameters of the model, for example, in a Dupire (1992) model, the local volatility surface) and market risks (the sensitivities to the market variables, for example, the implied volatility surface). Model parameters are typically calibrated to market variables, sometimes analytically (see Dupire’s formula expressing a local volatility as a function of the implied volatilities) but mostly numerically, where the model parameters are iteratively set to minimize the (generally, squared) error to market instruments. In machine learning lingo, the model learns its parameters by calibration to market instruments (underlying assets and European options) and applies them to off-market instruments (exotics). The value of a transaction is an explicit (although, most of the time, numerical) function ...
https://www.ssrn.com/abstract=3262571
https://www.ssrn.com/1739424.htmlThu, 15 Nov 2018 20:06:01 GMTREVISION: From Model to Market Risks: The Implicit Function Theorem (IFT) DemystifiedOne persisting conundrum in the theory and practice of quantitative risk management models is the relationship of model risks (the risk sensitivities of a transaction or set of transactions to the parameters of the model, for example, in a Dupire (1992) model, the local volatility surface) and market risks (the sensitivities to the market variables, for example, the implied volatility surface). Model parameters are typically calibrated to market variables, sometimes analytically (see Dupire’s formula expressing a local volatility as a function of the implied volatilities) but mostly numerically, where the model parameters are iteratively set to minimize the (generally, squared) error to market instruments. In machine learning lingo, the model learns its parameters by calibration to market instruments (underlying assets and European options) and applies them to off-market instruments (exotics). The value of a transaction is an explicit (although, most of the time, numerical) function ...
https://www.ssrn.com/abstract=3262571
https://www.ssrn.com/1735012.htmlWed, 31 Oct 2018 10:02:51 GMTREVISION: Theory of VolatilityBruno Dupire defined in 1993 an extension of Black & Scholes’s model consistent with the market implied volatility smile on equity derivatives. This paper shows how Dupire’s theory can be extended to take into account more realistic assumptions, such as interest rates, dividends, stochastic volatility and jumps. Moreover, we describe a similar theory in an interest rate setting, deriving a “caplet smile” formula, similar to Dupire’s “local volaitlity” formula, defining an extended Vasicek model consistent with the market implied caplet smile. Our results also lead to an efficient calibration strategy for Markov interest rate models, and they are likely to considerably speed-up the calibration of non Gaussian models, with or without taking into account the volatility smile.
The first section introduces the fundamental mathematical concepts and notations, and presents a few applications. It also re-derives Dupire’s formula in a different context. The second section shows how Dupire’s ...
https://www.ssrn.com/abstract=3173772
https://www.ssrn.com/1693507.htmlFri, 18 May 2018 15:47:05 GMT