Insurance Mathematics and Stochastic Finance

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Talks in Financial and Insurance Mathematics

This is the regular weekly research seminar on Insurance Mathematics and Stochastic Finance.

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Spring Semester 2019

Date / Time Speaker Title Location
7 March 2019
17:15-18:15 		Julien Trufin
Université Libre de Bruxelles
Event Details

Talks in Financial and Insurance Mathematics

Title Multivariate Credibility Modeling For Usage-based Motor Insurance Pricing With Behavioral Data
Speaker, Affiliation Julien Trufin, Université Libre de Bruxelles
Date, Time 7 March 2019, 17:15-18:15
Location HG G 43
Abstract Pay-How-You-Drive (PHYD) or Usage-Based (UB) systems for automobile insurance provide actuaries with behavioral risk factors, such as the time of the day, average speeds and other driving habits. These data are collected while the contract is in force with the help of telematic devices installed in the vehicle. They thus fall in the category of a posteriori information that becomes available after contract initiation. For this reason, they must be included in the actuarial pricing by means of credibility updating mechanisms instead of being incorporated in the score as ordinary a priori observable features. This talk proposes the use of multivariate mixed models to describe the joint dynamics of telematics data and claim frequencies. Future premiums, incorporating past experience can then be determined using the predictive distribution of claim characteristics given past history. This approach allows the actuary to deal with the variety of situations encountered in insurance practice, ranging from new drivers without telematics record to contracts with different seniority and drivers using their vehicle to different extent, generating varied volumes of telematics data.
Multivariate Credibility Modeling For Usage-based Motor Insurance Pricing With Behavioral Dataread_more
HG G 43
21 March 2019
17:15-18:15 		Christa Cuchiero
Universität Wien
Event Details

Talks in Financial and Insurance Mathematics

Title goMATH: Statistics and modeling of rough covariance processes
Speaker, Affiliation Christa Cuchiero, Universität Wien
Date, Time 21 March 2019, 17:15-18:15
Location HG G 43
Abstract The rough volatility paradigm asserts that the trajectories of assets' volatility are rougher than Brownian motion, a revolutionary perspective that has changed certain persistent views of volatility. It provides via stochastic Volterra processes a universal approach to capture important features of time series and option price data as well as microstructural foundations of markets. We first shed light on rough volatility and on rough covariance between two assets from an econometric angle, focusing on statistical properties of rough spot covariance estimation with Fourier methods. We then provide modeling approaches beyond the univariate case via rough affine covariance models, in particular a multivariate rough Heston type model based on a rough Wishart process.
goMATH: Statistics and modeling of rough covariance processesread_more
HG G 43
28 March 2019
17:15-18:15 		Dominykas Norgilas
University of Warwick
Event Details

Talks in Financial and Insurance Mathematics

Title Pricing and hedging of American puts under model uncertainty
Speaker, Affiliation Dominykas Norgilas, University of Warwick
Date, Time 28 March 2019, 17:15-18:15
Location HG G 43
Abstract In a two-period setting we derive the model-independent upper bound of the American put option. The model associated with the highest price of the American put is the extended left-curtain martingale coupling. Moreover we derive the cheapest superhedge.
Pricing and hedging of American puts under model uncertaintyread_more
HG G 43
4 April 2019
17:15-18:15 		Pietro Millossovich
CASS Business School
Event Details

Talks in Financial and Insurance Mathematics

Title Cascade Sensitivity Measures (joint with S. Pesenti and A. Tsanakas)
Speaker, Affiliation Pietro Millossovich, CASS Business School
Date, Time 4 April 2019, 17:15-18:15
Location HG G 43
Abstract Sensitivity measures quantify the extent to which the distribution of a model output is affected by small changes (stresses) in an individual random input factor. For input factors that are dependent, a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, which captures the direct impact of the stressed input factor on the output, as well as indirect effects via other input factors that are dependent on the one being stressed. In this way, the dependence between inputs is explicitly taken into account. Representations of the cascade sensitivity measure, which can be calculated from a single Monte Carlo sample, are provided for two types of stress: a) a perturbation of the distribution of an input factor, such that the stressed input follows a mixture distribution, and b) an additive random shock applied to the input factor. These representations do not require simulations under different model specifications or the explicit study of the properties of the model’s aggregation function, making the proposed method attractive for practical applications, as is illustrated through numerical examples.
Cascade Sensitivity Measures (joint with S. Pesenti and A. Tsanakas)read_more
HG G 43
18 April 2019
15:15-16:15 		Steve Kou
Boston University
Event Details

Talks in Financial and Insurance Mathematics

Title A Theory of FinTech
Speaker, Affiliation Steve Kou, Boston University
Date, Time 18 April 2019, 15:15-16:15
Location HG G 19.1
Abstract In this talk I will give a brief overview of current academic research on Fintech by using tools from mathematics and statistics. The topics to be discussed include:
(1) P2P equity financing: how to design contracts suitable for a P2P equity financing platform with information asymmetry.
(2) Designing stable coins: how to design stable cryptocurrency by using option pricing theory.
(3) Crowd Wisdom and Prediction Markets: how to use the collective opinion of a group to make predictions.
(4) Data privacy preservation: how to do econometrics based on the encrypted data while still preserving privacy. All the above 4 topics are based on my recent papers.
A Theory of FinTechread_more
HG G 19.1
2 May 2019
17:15-18:15 		Prof. Dr. Xiaolu Tan
CEREMADE -University of Paris-Dauphin
Event Details

Talks in Financial and Insurance Mathematics

Title Mean Field Games with Branching
Speaker, Affiliation Prof. Dr. Xiaolu Tan, CEREMADE -University of Paris-Dauphin
Date, Time 2 May 2019, 17:15-18:15
Location HG G 43
Abstract Mean field games are concerned with the limit of large-population stochastic differential games where the agents interact through their empirical distribution. In the classical setting, the number of players is large but fixed throughout the game. However, in various applications, such as population dynamics or economic growth, the number of players can vary across time which may lead to different Nash equilibria. For this reason, we introduce a branching mechanism in the population of agents and obtain a new formulation of mean field games. We then study the problem with both PDE and probabilistic approach. Joint work with Julien Claisse and Zhenjie Ren.
Mean Field Games with Branchingread_more
HG G 43
9 May 2019
17:15-18:15 		Martin Tegner
Oxford University
Event Details

Talks in Financial and Insurance Mathematics

Title Machine Learning For Volatility
Speaker, Affiliation Martin Tegner, Oxford University
Date, Time 9 May 2019, 17:15-18:15
Location HG G 43
Abstract The main focus of this talk will be a nonparametric approach for local volatility. We look at the calibration problem in a probabilistic framework based on Gaussian process priors. This gives a way of encoding prior believes about the local volatility function and a model which is flexible yet not prone to overfitting. Besides providing a method for calibrating a (range of) point-estimate(s), we draw posterior inference from the distribution over local volatility. This leads to a principled understanding of uncertainty attached with the calibration. Further, we seek to infer dynamical properties of local volatility by augmenting the input space with a time dimension. Ideally, this provides predictive distributions not only locally, but also for entire surfaces forward in time. We apply our approach to S&P 500 market data. In the final part of the talk we will give a short account of a nonparametric approach to modelling realized volatility. Again we take a probabilistic view and formulate a hypothesis space of stationary processes for volatility based on Gaussian processes.
Machine Learning For Volatilityread_more
HG G 43
16 May 2019
17:15-18:15 		Francesca Biagini
Mathematisches Institut der Universität München
Event Details

Talks in Financial and Insurance Mathematics

Title Liquidity induced asset bubbles in banking networks
Speaker, Affiliation Francesca Biagini, Mathematisches Institut der Universität München
Date, Time 16 May 2019, 17:15-18:15
Location HG G 43
Abstract We consider a constructive model for asset price bubbles, where the market price W is endogenously determined by the trading activity on the market and the fundamental price F is exogenously given, as in [3]. To justify F from a fundamental point of view, we embed this constructive approach in the martingale theory of bubbles, see [4] and [1], by showing the existence of a flow of equivalent martingale measures for W, under which F equals the expectation of the discounted future cash flow. As an application, we study bubble formation and evolution in a financial network. This talk is based on the paper [2].
[1] F. Biagini, H. Foellmer, and S. Nedelcu. Shifting martingale measuresand the slow birth of a bubble. Finance and Stochastics, 18(2):297-326,2014.
[2] Biagini, F., Mazzon, A., Meyer-Brandis, T. (2018) Liquidityinduced asset bubbles via flows of ELMMs, SIAM Journal on FinancialMathematics 9 (2).
[3] R. Jarrow, P. Protter, and A. Roch. A Liquidity Based Model for AssetPrice. Quantitative Finance, 12(1):1339-1349, 2012.
[4] R. Jarrow, P. Protter, and K. Shimbo. Asset price bubbles inincomplete markets. Mathematical Finance, 20(2):145-185, 2010.
Liquidity induced asset bubbles in banking networksread_more
HG G 43
23 May 2019
17:15-18:15 		Martin Keller-Ressel
Technische Universität Dresden: Institut für Mathematische Stochastik
Event Details

Talks in Financial and Insurance Mathematics

Title A Comparison Principle Between Rough and Non-Rough Heston Models
Speaker, Affiliation Martin Keller-Ressel, Technische Universität Dresden: Institut für Mathematische Stochastik
Date, Time 23 May 2019, 17:15-18:15
Location HG G 43
Abstract We present a new comparison principle, which allows to compare moment explosion times, moment generating functions and the slope of implied volatility between rough and non-rough Heston models. Essentially, the deterministically time-changed moment generating function of the non-rough Heston model provides a lower bound for the moment generating function of the rough Heston model outside of the interval [0,1]. Moreover, our results improve the lower bound for moment explosion times of Gerhold et al. (2018) for most parameter values of the Heston model. These results can be directly transferred to a comparison principle for the asymptotic slope of implied volatility between rough and non-rough Heston models. We also discuss to which extent our results can be extended from rough models defined through power-law convolution kernels to other convolution kernels. The talk is based on joint work with Assad Majid.
A Comparison Principle Between Rough and Non-Rough Heston Modelsread_more
HG G 43
30 May 2019
Event Details

Talks in Financial and Insurance Mathematics

Title Ascension Day
Speaker, Affiliation
Date, Time 30 May 2019,
Location
Ascension Day
13 June 2019
17:15-18:15
Event Details

Talks in Financial and Insurance Mathematics

Title TBA: Talk
Speaker, Affiliation
Date, Time 13 June 2019, 17:15-18:15
Location HG G 43
TBA: Talk
HG G 43
18 June 2019
17:15-18:15 		Seyoung Park
Loughborough University
Event Details

Talks in Financial and Insurance Mathematics

Title A Generalization of Friedman's Permanent Income Hypothesis with a Large, Negative Income Shock
Speaker, Affiliation Seyoung Park, Loughborough University
Date, Time 18 June 2019, 17:15-18:15
Location HG G 19.1
Abstract We generalize the Permanent Income Hypothesis (PIH) of Friedman (1957) with a large, negative income shock. The generalized PIH can explain how households would struggle with such a disastrous income shock, which is consistent with the recent empirical findings. We develop an optimal consumption and investment framework for achieving consumption smoothing in the presence of the large, negative income shock. We also provide a general equilibrium analysis shedding new light on interest rate. We find that risk premium associated with the large, negative income shock would be a leading cause of today's low interest rate environment. Authors: Steven Kou (Boston University) and Seyoung Park (Loughborough University)
A Generalization of Friedman's Permanent Income Hypothesis with a Large, Negative Income Shockread_more
HG G 19.1

Organizers: Anastasis Kratsios

Archive: SS 24 AS 23 SS 23 AS 22 SS 22 AS 21 SS 21 SS 20 AS 19 SS 19 AS 18 SS 18 AS 17 SS 17 AS 16 SS 16 AS 15 SS 15 AS 14 SS 14 AS 13 SS 13 AS 12 SS 12 AS 11 SS 11 AS 10 SS 10 AS 09

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