Volume 2020, Issue 110. Pages 1–84
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In this issue:
Bibliography
- “Contents,” Wilmott, vol. 2020, iss. 110, p. 1–1, 2020.
[Bibtex]@article {WILM:WILM10879, title = {Contents}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10879}, doi = {10.1002/wilm.10879}, pages = {1--1}, year = {2020}, } - D. Tudball, “From the bottom to the top,” Wilmott, vol. 2020, iss. 110, p. 2–3, 2020.
[Bibtex] [Abstract]
The market for short‐duration loans has note taken advantage of the revolutionary developments in precision time‐keeping technology.
@article {WILM:WILM10880, author = {Tudball, Dan}, title = {From the Bottom to the Top}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10880}, doi = {10.1002/wilm.10880}, pages = {2--3}, year = {2020}, abstract = {The market for short‐duration loans has note taken advantage of the revolutionary developments in precision time‐keeping technology.}, } - “News,” Wilmott, vol. 2020, iss. 110, p. 4–9, 2020.
[Bibtex]@article {WILM:WILM10881, title = {News}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10881}, doi = {10.1002/wilm.10881}, pages = {4--9}, year = {2020}, } - M. Kelly, “Symbolic computation in financial engineering,” Wilmott, vol. 2020, iss. 110, p. 10–25, 2020.
[Bibtex] [Abstract]
Wolfram language is capable of representing a wide array of financial instruments and analyses as symbolic elements, thereby providing exact solutions.
@article {WILM:WILM10882, author = {Kelly, Michael}, title = {Symbolic Computation in Financial Engineering}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10882}, doi = {10.1002/wilm.10882}, pages = {10--25}, year = {2020}, abstract = {Wolfram language is capable of representing a wide array of financial instruments and analyses as symbolic elements, thereby providing exact solutions.}, } - A. Brown, “It's still not the heat,” Wilmott, vol. 2020, iss. 110, p. 26–29, 2020.
[Bibtex] [Abstract]
Five years on from the author's article, “It's Not the Heat, It's the Tepidity,” did the future turn out to be very different from the data, as Intergovernmental Panel of Climate Change models would have had us believe?
@article {WILM:WILM10883, author = {Brown, Aaron}, title = {It's Still Not the Heat}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10883}, doi = {10.1002/wilm.10883}, pages = {26--29}, year = {2020}, abstract = {Five years on from the author's article, “It's Not the Heat, It's the Tepidity,” did the future turn out to be very different from the data, as Intergovernmental Panel of Climate Change models would have had us believe?}, } - R. Poulsen, “Magical,” Wilmott, vol. 2020, iss. 110, p. 30–33, 2020.
[Bibtex] [Abstract]
Recent applications of machine learning in option pricing.)
@article {WILM:WILM10884, author = {Poulsen, Rolf}, title = {Magical}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10884}, doi = {10.1002/wilm.10884}, pages = {30--33}, year = {2020}, abstract = {Recent applications of machine learning in option pricing.)}, } - U. Wystup, “Mixed local volatility boosts distribution of exotics,” Wilmott, vol. 2020, iss. 110, p. 34–37, 2020.
[Bibtex] [Abstract]
Speed, flexible calibration, and market acceptance make MLVs a necessary tool in the FX quant's memory.
@article {WILM:WILM10885, author = {Wystup, Uwe}, title = {Mixed Local Volatility Boosts Distribution of Exotics}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10885}, doi = {10.1002/wilm.10885}, pages = {34--37}, year = {2020}, abstract = {Speed, flexible calibration, and market acceptance make MLVs a necessary tool in the FX quant's memory.}, } - D. Tudball, “Agents of change,” Wilmott, vol. 2020, iss. 110, p. 38–45, 2020.
[Bibtex] [Abstract]
Is it time for quantitative finance to re‐examine its relationship (or lack of one) with agent‐based modeling?
@article {WILM:WILM10886, author = {Tudball, Dan}, title = {Agents of Change}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10886}, doi = {10.1002/wilm.10886}, pages = {38--45}, year = {2020}, abstract = {Is it time for quantitative finance to re‐examine its relationship (or lack of one) with agent‐based modeling?}, } - R. Bogni, “This strange new wealth tax,” Wilmott, vol. 2020, iss. 110, p. 46–47, 2020.
[Bibtex] [Abstract]
Monetary policy, QE, and negative interest rates have introduced a nearly global tax, whose beneficiaries are uncertain.
@article {WILM:WILM10887, author = {Bogni, Rudi}, title = {This Strange New Wealth Tax}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10887}, doi = {10.1002/wilm.10887}, pages = {46--47}, year = {2020}, abstract = {Monetary policy, QE, and negative interest rates have introduced a nearly global tax, whose beneficiaries are uncertain.}, } - L. MacLean and B. Ziemba, “Review of the nfl 2019–20 season, playoffs, and super bowl,” Wilmott, vol. 2020, iss. 110, p. 48–59, 2020.
[Bibtex] [Abstract]
Looking back at a season that as fortunate to complete and assessing how the authors' ELO rating system fared.
@article {WILM:WILM10888, author = {MacLean, Leonard and Ziemba, Bill}, title = {Review of the NFL 2019–20 Season, Playoffs, and Super Bowl}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10888}, doi = {10.1002/wilm.10888}, pages = {48--59}, year = {2020}, abstract = {Looking back at a season that as fortunate to complete and assessing how the authors' ELO rating system fared.}, } - P. Jöckel, “Time‐weighted volatility,” Wilmott, vol. 2020, iss. 110, p. 60–65, 2020.
[Bibtex] [Abstract]
We discuss the question of weekend and holiday market volatility in the options markets, its implications for the temporal interpolation of implied volatility, and the ramifications for numerical theta computations for the purpose of commercial profit and loss (P&L) explanations. We give a practical methodology to accommodate these observations and requirements in a derivatives trading environment, and compare what this method implies for the future evolution of so‐called “overnight” options in the foreign exchange market with actual market‐observed time series of such traded instruments' Black implied volatilities.
@article {WILM:WILM10889, author = {Jöckel, Peter}, title = {Time‐Weighted Volatility}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10889}, doi = {10.1002/wilm.10889}, pages = {60--65}, keywords = {time‐weighted options volatility, out of hours, weekend volatility, numerical theta, P&L explanation}, year = {2020}, abstract = {We discuss the question of weekend and holiday market volatility in the options markets, its implications for the temporal interpolation of implied volatility, and the ramifications for numerical theta computations for the purpose of commercial profit and loss (P&L) explanations. We give a practical methodology to accommodate these observations and requirements in a derivatives trading environment, and compare what this method implies for the future evolution of so‐called “overnight” options in the foreign exchange market with actual market‐observed time series of such traded instruments' Black implied volatilities.}, } - D. J. Duffy, “Analysis of covid‐19 mathematical and software models: or how not to set up a softward project,” Wilmott, vol. 2020, iss. 110, p. 66–71, 2020.
[Bibtex] [Abstract]
This report discusses the open‐source software that implements the COVID‐19 model in [1] (announced March 16, 2020), led by Dr. Neil Ferguson of Imperial College London (ICL). My personal interest was to investigate how the model was implemented after having seen it described as a system of ordinary differential equations (ODEs) on BBC News on March 17, 2020. Anecdotal evidence suggests that the original program (written in C) is at least 20 years old and it is undocumented (nothing new in the software world; the programmers in this case probably thought it was not necessary to write readable and maintainable software). Furthermore, all the 15,000 lines of code were in a single file (sometimes called balls of mud). On April 22, 2020 a modified version (called 0.7.0, seemingly produced by Microsoft) appeared consisting of approximately 12 separate source files. This is the version of the program that we review in this article. We do not investigate the fixes that have been made between April 22 and the time of writing of this report. Finally, we note that version 0.7.0 is not an implementation of the ODEs that were announced on BBC News with great aplomb on the evening of Saint Patrick's Day 2020. For a discussion of ODEs in epidemiology, see [2]. Before I discovered that the ICL model was not ODE‐based (seemingly contradicting the BBC announcement) I solved the MSEIR (iMmune, Susceptible, Exposed, Infective, Recovered) ODE model numerically in C++. The system of equations is relatively benign and we used the C++ Boost odeint library to solve them. (A word of advice: it is tempting to use the Euler method but don't use it, not even for producing cute S‐curves in your blogs.)
@article {WILM:WILM10890, author = {Duffy, Daniel J.}, title = {Analysis of COVID‐19 Mathematical and Software Models: Or How NOT to Set Up a Softward Project}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10890}, doi = {10.1002/wilm.10890}, pages = {66--71}, keywords = {C language, big balls of mud, software crisis, software quality metrics, random number generation, parallel code (speedup, race conditions), software maintenance, defined software process}, year = {2020}, abstract = {This report discusses the open‐source software that implements the COVID‐19 model in [1] (announced March 16, 2020), led by Dr. Neil Ferguson of Imperial College London (ICL). My personal interest was to investigate how the model was implemented after having seen it described as a system of ordinary differential equations (ODEs) on BBC News on March 17, 2020. Anecdotal evidence suggests that the original program (written in C) is at least 20 years old and it is undocumented (nothing new in the software world; the programmers in this case probably thought it was not necessary to write readable and maintainable software). Furthermore, all the 15,000 lines of code were in a single file (sometimes called balls of mud). On April 22, 2020 a modified version (called 0.7.0, seemingly produced by Microsoft) appeared consisting of approximately 12 separate source files. This is the version of the program that we review in this article. We do not investigate the fixes that have been made between April 22 and the time of writing of this report. Finally, we note that version 0.7.0 is not an implementation of the ODEs that were announced on BBC News with great aplomb on the evening of Saint Patrick's Day 2020. For a discussion of ODEs in epidemiology, see [2]. Before I discovered that the ICL model was not ODE‐based (seemingly contradicting the BBC announcement) I solved the MSEIR (iMmune, Susceptible, Exposed, Infective, Recovered) ODE model numerically in C++. The system of equations is relatively benign and we used the C++ Boost odeint library to solve them. (A word of advice: it is tempting to use the Euler method but don't use it, not even for producing cute S‐curves in your blogs.)}, } - C. M. Puetter and S. Renzitti, “One‐factor hull—white model calibration for cva part i: instrument selection with a kink,” Wilmott, vol. 2020, iss. 110, p. 72–76, 2020.
[Bibtex] [Abstract]
This paper is the first of a multi‐part series on the calibration of the one‐factor Hull—White short rate model for the purpose of computing CVAs (and XVas) with an XVa system. It introduces an atypical bootstrapping scheme for the calibration of the short rate volatility. The second part focuses on the selection of the mean reversion parameter. In both expositions we present long‐term time series results for EUR, JPY, and USD, covering the period from the beginning of 2009 (at the earliest) to spring 2020.
@article {WILM:WILM10891, author = {Puetter, Christoph M. and Renzitti, Stefano}, title = {One‐Factor Hull—White Model Calibration for CVA Part I: Instrument Selection with a Kink}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10891}, doi = {10.1002/wilm.10891}, pages = {72--76}, keywords = {XVA, Hull—White interest rate model, instrument selection for calibration, time series}, year = {2020}, abstract = {This paper is the first of a multi‐part series on the calibration of the one‐factor Hull—White short rate model for the purpose of computing CVAs (and XVas) with an XVa system. It introduces an atypical bootstrapping scheme for the calibration of the short rate volatility. The second part focuses on the selection of the mean reversion parameter. In both expositions we present long‐term time series results for EUR, JPY, and USD, covering the period from the beginning of 2009 (at the earliest) to spring 2020.}, } - C. M. Puetter and S. Renzitti, “One‐factor hull—white model calibration for cva part ii: optimizing the mean reversion parameter,” Wilmott, vol. 2020, iss. 110, p. 77–81, 2020.
[Bibtex] [Abstract]
This paper is the second of a multi‐part series on the calibration of the one‐factor Hull—White short rate model for the purpose of computing CVAs (and XVas) with an XVa system. The first part introduces an atypical bootstrapping scheme for the calibration of the short rate volatility. The present second part focuses on the selection of the mean reversion parameter. In both expositions we present long‐term time series results for EUR, JPY, and USD, covering the period from the beginning of 2009 (at the earliest) to spring 2020.
@article {WILM:WILM10892, author = {Puetter, Christoph M. and Renzitti, Stefano}, title = {One‐Factor Hull—White Model Calibration for CVA Part II: Optimizing the Mean Reversion Parameter}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10892}, doi = {10.1002/wilm.10892}, pages = {77--81}, keywords = {XVA, Hull—White interest rate model, mean reversion parameter calibration, time series}, year = {2020}, abstract = {This paper is the second of a multi‐part series on the calibration of the one‐factor Hull—White short rate model for the purpose of computing CVAs (and XVas) with an XVa system. The first part introduces an atypical bootstrapping scheme for the calibration of the short rate volatility. The present second part focuses on the selection of the mean reversion parameter. In both expositions we present long‐term time series results for EUR, JPY, and USD, covering the period from the beginning of 2009 (at the earliest) to spring 2020.}, } - M. Radley, “Cars,” Wilmott, vol. 2020, iss. 110, p. 82–83, 2020.
[Bibtex] [Abstract]
The hardcore Lotus Exige Sport 350 gets some updates keep it as competitive as ever.
@article {WILM:WILM10893, author = {Radley, Milford}, title = {Cars}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10893}, doi = {10.1002/wilm.10893}, pages = {82--83}, year = {2020}, abstract = {The hardcore Lotus Exige Sport 350 gets some updates keep it as competitive as ever.}, } - J. Darasz, “The skewed world of jan darasz,” Wilmott, vol. 2020, iss. 110, p. 84–84, 2020.
[Bibtex]@article {WILM:WILM10894, author = {Darasz, Jan}, title = {The skewed world of Jan Darasz}, journal = {Wilmott}, volume = {2020}, number = {110}, publisher = {John Wiley & Sons, Ltd}, issn = {1541-8286}, url = {http://dx.doi.org/10.1002/wilm.10894}, doi = {10.1002/wilm.10894}, pages = {84--84}, year = {2020}, }
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