Editorial
Interview with Dr. Thorsten Schmidt, Senior Financial Engineer, MathFinance, and Professor for Mathematical Stochastics at University of Freiburg
What are recent challenges in the insurance sector?
The low interest rate regimes challenge insurance companies to a large extent, since the well-established model of insuring over a long-time horizon yielding an attractive outcome does not work anymore. The question arises, how a pension or similar insurance product can be achieved in a clever and not too expensive way.
An attractive way out is to link the payoff of insurance contracts to markets promising higher return (in particular over the long-time horizon), like equity markets. Variable annuities are products which do this and in addition provide attractive guarantees for the insured.
Pricing and hedging of these products require utilizing tools from insurance mathematics and mathematical finance. Compared to the well-established insurance valuation principles and the fundamental theorems of asset pricing with risk-neutral pricing, results linking both markets on a general level are rare. In a project with Philippe Artzner and Karl-Theodor Eisele, we establish a fundamental theorem of insurance valuation which propagates a simple rule: the QP-rule. Under this rule, pricing can be done by projection insurance claims to the financial market by taking expectations under P and then use classical risk-neutral pricing.
What are interesting products in this fields?
There are many types of variable-annuities (VA), with different forms of guarantees, different underlying instruments, etc. and the valuation is a challenge. VA’s are a huge market: in 2018 the sales in the U.S. are estimated at 100 billion USD.
Why is pricing so difficult?
VAs, equity-linked products link two rather unconnected fields. A deeper look shows that almost all insurance contract link to interest rate markets, such that the connection of these two fields is rather the rule than the exception. Pricing methodologies need to be market-consistent, i.e. they need to take risk-neutral pricing and available data on financial markets into account.
VAs, for example, link the performance of an index to insurance quantities, like lifetime of the insured and have optionality behaviour like embedded surrender options. Existing pricing rules work under quite restrictive assumptions: for example, mortality is assumed to be independent of the stock market, and so is surrender behaviour; leading to a large model risk.
What is different taking the Corona-pandemic into account? Do you still model mortality and stock prices the same way?
The corona crisis highlights how risky such an assumption can be: we have seen that high mortality is not independent from the financial markets – in a scenario with high mortality insurances have to deliver for such contracts, with embedded guarantees being largely in the money. A similar effect can be seen with surrender behaviour, which is at least to a certain extent driven by the performance of the underlying portfolio. We currently develop general approaches, like relying on affine models known from credit risk, to capture such behaviour and develop general valuation rules in this context.
You have won a significant prize, what is it and what did you do?
Together with Raquel Gaspar we were awarded a Research Grant “Ignadion H. de Larramendi” from Fundación MAPFRE. The topic of the research projects is to use affine processes for the valuation of insurance products which link to financial markets. The project consists of fundamental research together with showcases where we illustrate the applicability of the framework for practical valuation.
How did your interest in this topic started and what are recent activities?
Together with Ernst Eberlein in Freiburg, Philippe Artzner, Jean Berard and Karl-Theodor Eisle from Strasbourg we worked for two years in a joint project between the Freiburg Institute of Advanced Studies (FRIAS) and the University of Strasbourg Institute of Advanced Studies (USIAS) on the topic. We had several conferences and saw a large interest, both from scientists as well as from practitioners in the field.
Most notably, together with Hans-Jörg Albrecher, Francesca Biagini and Monique Jeanblanc we are organizing an Oberwolfach Workshop about New Challenges in the Interplay between Finance and Insurance. In this workshop we hope to foster research on this highly interesting but long neglected topic.
How can practitioners benefit from it?
The current valuation and hedging methodologies in the field are far from being as developed as the related methodologies on financial markets. This implies that risks in this field are far less understood and risk management is not sufficiently developed. Practitioners will be able to reduce risk significantly by better understanding how to price and hedge. On the other side, this will enable cheaper insurance offering highly attractive pay-off schemes. In my eyes, this could be a promising win-win situation for insured and insurers.
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Upcoming Events
MathFinance Conference 2020
1-2 September, 2020
Marriott Hotel, Canary Wharf, London
In view of the uncertainty caused by the Coronavirus outbreak, MathFinance has decided to postpone its 20th annual MathFinance Finance conference to 1st and 2nd September to guarantee a safe and successful participation. The location remains unchanged. A majority of speakers have confirmed their attendance and the full agenda will be uploaded in due course.
For more information please visit: https://www.mathfinance.com/events/mathfinance-conference-2020/
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MathFinance Trainings
MathFinance is excited to host its new training offerings for 2020. For further details please click on the links below. Additionally, we also offer in-house training programmes on areas of interest covering Machine Learning, FX Options, Interest Rate Derivatives and Equity Derivatives.
Course: Machine Learning & Artificial Intelligence Applications for Financial Markets
Trainer: Dr. Thorsten Schimdt, Senior Financial Engineer, MathFinance & Professor for mathematical stochastics at University of Freiburg
Date: 27-29 May 2020
Format: Online training course, using Webex
Fees: EUR 2,500 p.p. EUR 2,000 p.p. if 2 or more people join from the same organization
More information: Machine Learning & Artificial Intelligence Applications for Financial Markets
Course: FX Options & Structured Products
Trainer: Dr. Uwe Wystup, Managing Director, MathFinance AG
Date: 13-14 July 2020, Singapore time-zone
Format: Online training course, using Webex
Fees: SGD 3,000 p.p. SGD 2,500 p.p. if 2 or more people join from the same organization
More information: FX Options & Structured Products
To enrol for any of the MathFinance training courses or for further inquiries, please mail us at: info@mathfinance.com
For further details on our other offerings please visit: https://www.mathfinance.com/trainings/
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Machine Learning for Option Pricing, Calibration and Hedging
with Jörg Kienitz and Nikolai Nowaczyk
July 2-3rd, 2020
London
Cost: £1999
‘Early Bird’ Discount – 10% before 15 April, 2020
The goal of this two-day workshop is to provide a detailed overview of machine learning techniques applied for finance. We offer insights into the latest techniques of using such techniques for modelling financial markets where we focus on pricing and calibration.
We not only tackle the theory but give practical guidance and live demonstrations of the computational methods involved. After introducing the subject we cover Gaussian Process Regression and Artificial Neural Networks and show how such methods can be applied to solve option pricing problems, speed up the calculation of xVAs or apply them for hedging.
We further show how to use existing pricing libraries to interact with machine learning environments often set up in Python.
We explain how to set up the methods mainly in Python using Keras, Tensorflow or SciKit Learn. We give many examples which are directly related to financial mathematics and can be explored further after the course. All the material is available as Jupyther notebooks. For Gaussian Processes we use Matlab and Python examples.
This workshop covers the fundamentals and it illustrates the application of state-of-the-art machine learning applications for application to Mathematical Finance.
Further information, brochure and registration: http://bit.ly/ml-for-finance
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Digital Finance
Call for Papers
Special Issue on Artificial Intelligence, Machine Learning and Platform Innovation in Quantitative Finance (MathFinance Conference 2020)
Overview
Traditionally, Quantitative Finance has revolved around the development of parsimonious models that yield some economic understanding of financial markets. In recent years, there has been a change in this paradigm by embracing data-driven methods from AI and ML. Here are some reasons that explain this shift: greater amounts of financial data are available that require fast processing; financial analysis and computations are supplemented by non-financial data, such as textual data, in order to create new insights; data-driven methods allow to detect trends and market changes that would not be observed with a rigid model. At the same time, platform technology has taken over trading of spot and derivatives in financial markets. Pricing models, Greeks and risk calculation must be faster and more accurate than ever before. The special issue welcomes contributions that explore innovative uses of AI / ML methods and platform technology in Quantitative Finance. These can involve economic, quantitative, computational and technological aspects.
Speakers and participants of the MathFinance Conference 2020 are encouraged to submit their work, but the special issue also welcomes contributions from the community.
Editors of the Special Issue
Prof. Dr. Natalie Packham, Berlin School of Economics and Law
Prof. Dr. Uwe Wystup, Managing Director, MathFinance
Instructions for Submission
For submission, authors are requested to access the access the Editorial Manager at the following URL: http://www.editorialmanager.com/dfin/default.aspx. Please answer “Yes” when asked if your manuscript belongs to a special issue and select the special issue in the list that will pop up.
Potential authors are reminded that all papers that are finally accepted for this special issue will be subject to format restrictions complying with the publisher’s standards. To speed up publication, and to ensure a unified layout throughout the special issue, authors are kindly advised to use LaTeX. Springer’s LaTeX template (click here) can be used to prepare source files (please choose the formatting option “smallextended”). The authors are highly recommended not to modify the class file by introducing personal settings and/or definitions.
Important Dates
Deadline for paper submission: 30 June
First-round decisions: 31 July
Deadline for second-round submission: 15 September
Final decisions: 31 October
About Digital Finance
The journal is a top tier peer-reviewed academic and practitioner journal that publishes high-quality articles with a focus on digital finance and innovation as well as on the analysis of digital and internet innovations on financial services and the economy. The journal publishes theoretical or empirical, qualitative or quantitative papers of interest to academics, practitioners, and regulators with the emphasis on empirical, financial market, and investment innovation, financial policy research and recommendations related to improving the welfare in the digital economy. Further details on this journal are available on the Springer website: https://www.springer.com/42521