Departement Mathematik

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Talks in financial and insurance mathematics

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Frühjahrssemester 2020

Datum / Zeit Referent:in Titel Ort
20. Februar 2020
17:15-18:15 		Martin Herdegen
University of Warwick
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Talks in Financial and Insurance Mathematics

Titel A Dual Characterisation of Regulatory Arbitrage for Coherent Risk Measure
Referent:in, Affiliation Martin Herdegen, University of Warwick
Datum, Zeit 20. Februar 2020, 17:15-18:15
Ort HG G 43
Abstract We revisit portfolio selection in a one-period financial market under a coherent risk measure constraint, the most prominent example being Expected Shortfall (ES). Unlike in the case of classical mean-variance portfolio selection, it can happen that no efficient portfolios exist. We call this situation regulatory arbitrage. We show that the presence or absence of regulatory arbitrage is intimately linked to the interplay between the set of equivalent martingale measures (EMMs) for the discounted risky assets and the set of absolutely continuous measures in the dual characterisation of the risk measure. In the special case of ES, our results show that the market does not admit regulatory arbitrage for ES at confidence level $\alpha$ if and only if there exists an EMM $Q \approx P$ such that $\Vert \frac{dQ}{dP} \Vert_\infty < \frac{1}{\alpha}$. The talk is based on joint work with my PhD student Nazem Khan.
A Dual Characterisation of Regulatory Arbitrage for Coherent Risk Measureread_more
HG G 43
27. Februar 2020
17:15-18:15 		Alexander J. McNeil
The York Management School, University of York (UK)
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Talks in Financial and Insurance Mathematics

Titel Modelling Volatility with V-Transforms
Referent:in, Affiliation Alexander J. McNeil, The York Management School, University of York (UK)
Datum, Zeit 27. Februar 2020, 17:15-18:15
Ort HG G 43
Abstract An approach to the modelling of financial return series using a class of uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the return distribution and quantiles of the distribution of a predictable volatility proxy variable constructed as a function of the return. V-transforms can be represented as copulas and permit the construction and estimation of models that combine arbitrary marginal distributions with linear or non-linear time series models for the dynamics of the volatility proxy. The idea is illustrated using a transformed Gaussian ARMA process for volatility, yielding the class of VT-ARMA copula models. These can replicate many of the stylized facts of financial return series and facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk. Estimation of models is carried out by adapting the exact maximum likelihood approach to the estimation of ARMA processes.
Modelling Volatility with V-Transformsread_more
HG G 43
19. März 2020
17:15-18:15 		Agostino Capponi
Columbia University
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Talks in Financial and Insurance Mathematics

Titel Large Orders in Small Markets: On Optimal Execution with Endogenous Liquidity Supply
Referent:in, Affiliation Agostino Capponi, Columbia University
Datum, Zeit 19. März 2020, 17:15-18:15
Ort HG G 43
Abstract CANCELLED: We solve a continuous time dynamic Stackelberg game, where a large uninformed seller executes optimally, fully cognizant of the response of Cournot-competitive market makers. The game therefore endogenizes both demand and supply of liquidity. We provide closed form solutions for the value functions of market makers and large seller, the equilibrium bid/ask price, and the execution intensity. The closed-form solution yields several insights. First, stealth trading is both privately and socially costly because market makers incur additional costs not knowing when execution ends. Second, the presence of a large seller does not unambiguously benefit other participants. Market makers benefit only if there is enough risk-absorption capacity or if the execution period is short. Other investors benefit only when the seller sells at high enough intensity. The model explains quantitatively several empirical facts: order duration and participation rate correlate negatively, and price pressure subsides before execution ends. (based on joint work with Hongzhong Zhang and Albert Menkveld).
Large Orders in Small Markets: On Optimal Execution with Endogenous Liquidity Supplyread_more (ABGESAGT)
HG G 43
25. März 2020
16:00-17:15 		George Tzougas
London School of Economics
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Talks in Financial and Insurance Mathematics

Titel An EM Algorithm for Fitting Mixed Exponential Regression Models with Varying Dispersion
Referent:in, Affiliation George Tzougas, London School of Economics
Datum, Zeit 25. März 2020, 16:00-17:15
Ort Zoom
Abstract Regression modelling involving heavy-tailed response distributions, which have heavier tails than the exponential distribution, has become increasingly popular in many insurance settings including non-life insurance. Mixed Exponential models can be considered as a natural choice for the distribution of heavy-tailed claim sizes since their tails are not exponentially bounded. This paper is concerned with introducing a general family of mixed Exponential regression models with varying dispersion which can efficiently capture the tail behaviour of losses. Our main achievement is that we present an Expectation-Maximization (EM) type algorithm which can facilitate maximum likelihood (ML) estimation for our class of mixed Exponential models which allows for regression specifications for both the mean and dispersion parameters. Finally, a real data application based on motor insurance data is given to illustrate the versatility of the proposed EM type algorithm. Keywords:Mixed Exponential Distributions; EM Algorithm; Regression Models for the Mean and Dispersion parameters; Non-Life Insurance; Heavy-Tailed Losses
An EM Algorithm for Fitting Mixed Exponential Regression Models with Varying Dispersionread_more
Zoom
2. April 2020
17:15-18:15 		Antonis Papapantoleon
National Technical University of Athens
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Talks in Financial and Insurance Mathematics

Titel CANCELLED!! TBA
Referent:in, Affiliation Antonis Papapantoleon, National Technical University of Athens
Datum, Zeit 2. April 2020, 17:15-18:15
Ort HG G 43
CANCELLED!! TBA
HG G 43
8. April 2020
16:00-17:15 		TBA

Details

Talks in Financial and Insurance Mathematics

Titel Title T.B.A.
Referent:in, Affiliation TBA ,
Datum, Zeit 8. April 2020, 16:00-17:15
Ort HG G 43
Title T.B.A.
HG G 43
23. April 2020
16:00-17:00 		Corina Constantinescu
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Talks in Financial and Insurance Mathematics

Titel Stochastic Mortality Modelling for Dependent Coupled Lives
Referent:in, Affiliation Corina Constantinescu,
Datum, Zeit 23. April 2020, 16:00-17:00
Ort HG G 43
Abstract Broken-heart syndrome is the most common form of short-term dependence, inducing a temporary increase in an individual’s force of mortality upon the occurrence of extreme events, such as the loss of a spouse. Socioeconomic influences on bereavement processes allow for suggestion of variability in the significance of short-term dependence between couples in countries of differing levels of economic development. Motivated by analysis of a Ghanaian data set, we propose a stochastic mortality model of the joint mortality of paired lives and the causal relation between their death times, in a less economically developed country than those considered in existing studies. The paired mortality intensities are assumed to be non-mean-reverting Cox–Ingersoll–Ross processes, reflecting the reduced concentration of the initial loss impact apparent in the data set. The effect of the death on the mortality intensity of the surviving spouse is given by a mean-reverting Ornstein–Uhlenbeck process which captures the subsiding nature of the mortality increase characteristic of broken-heart syndrome. Inclusion of a population wide volatility parameter in the Ornstein–Uhlenbeck bereavement process gives rise to a significant non-diversifiable risk, heightening the importance of the dependence assumption in this case. Applying the model proposed to an insurance pricing problem, we obtain the appropriate premium under consideration of dependence between coupled lives through application of the indifference pricing principle. Joint presentation with Kira Henshaw and Olivier Menoukeu-Pamen
Stochastic Mortality Modelling for Dependent Coupled Livesread_more
HG G 43
30. April 2020
16:00-17:00 		Michal Pesta
(Charles University Prague)
Details

Talks in Financial and Insurance Mathematics

Titel Infinitely Stochastic Micro Forecasting
Referent:in, Affiliation Michal Pesta, (Charles University Prague)
Datum, Zeit 30. April 2020, 16:00-17:00
Ort HG G 43
Abstract Forecasting costs is now a front burner in empirical economics. We propose an unconventional tool for stochastic prediction of future expenses based on the individual (micro) developments of recorded events. Consider a firm, enterprise, institution, or state, which possesses knowledge about particular historical events. For each event, there is a series of several related subevents: payments or losses spread over time, which all leads to an infinitely stochastic process at the end. Nevertheless, the issue is that some already occurred events do not have to be necessarily reported. The aim lies in forecasting future subevent flows coming from already reported, occurred but not reported, and yet not occurred events. Our methodology is illustrated on quantitative risk assessment, however, it can be applied to other areas such as startups, epidemics, war damages, advertising and commercials, digital payments, or drug prescription as manifested in the talk. As a theoretical contribution, inference for infinitely stochastic processes is developed. In particular, a non-homogeneous Poisson process with non-homogeneous Poisson processes as marks is used, which includes for instance the Cox process as a special case.
Infinitely Stochastic Micro Forecastingread_more
HG G 43
14. Mai 2020
16:15-17:15 		Martin Bladt
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Talks in Financial and Insurance Mathematics

Titel Time series copula models with v-transforms: an alternative to GARCH modelling
Referent:in, Affiliation Martin Bladt,
Datum, Zeit 14. Mai 2020, 16:15-17:15
Ort HG
Abstract We propose a general class of time series models to model return volatility. The main idea is to use a V-transform to relate the original time series to a volatility proxy, which can be modelled using copula time series processes. Using a construction that we refer to as stochastic inversion of a V-transform, we develop processes that can model the dynamics of the conditional mean as well as the volatility of return data. We illustrate the class of models using ARMA and D-vine copula processes for the implied volatility proxies. Using real world data, we show that these models are competitive and can sometimes outperform popular models from the GARCH family.
Time series copula models with v-transforms: an alternative to GARCH modellingread_more
HG

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