Graduate Seminar Financial- and Actuarial Mathematics LMU and TUM (WS 2017/18)

 

 

Dates - December

Monday, 4.12.2017 - 1.Speech

Speaker: Prof. Dr. Alexander Szimayer

Topic: Rating Under Asymmetric Information (Christian Hilpert, Stefan Hirth, Alexander Szimayer)

Time: 14:15 o'clock

Place: Room 2.02.01, Parkring 11, 85748 Garching-Hochbrück

Description: We analyze how a firm’s reputation and track record affect its rating and cost of debt. We model a setting in which outsiders such as a rating agency and the firm’s creditors continuously update their assessment of the firm’s true state described by its cash flow. They observe the latter only imperfectly due to asymmetric information. Other things equal, the rating agency optimally rates a firm with the same observed cash flow higher, if the historical minimum is sufficiently low. Thus, the rating is not only driven by the most recent information, but history matters. The rating agency refines its unbiased cash flow estimate by ruling out the most overestimated types, leading to an overestimation at default. In response, the firm delays default and lower asset values are available to creditors upon default.

Monday, 4.12.2017 - 2.Speech

Speaker: Prof. Harry Joe

Topic: Estimation of tail dependence coefficients and extreme joint tail probabilities

Time: 15:00 o'clock

Place: Room 2.02.01, Parkring 11, 85748 Garching-Hochbrück

Description: Let C be a d-dimensional copula. With a random sample from this copula, several methods are introduced for estimation of the upper and lower tail dependence coefficients, as well as extreme joint tail probabilities such as the probability that all variables exceed their 0.99 quantiles and all variables are below their 0.01 quantiles. The main theory is based on (i) a tail expansion of the distribution D() of maximum or minimum of the random vector on the copula scale and (ii) a tail expansion of an integral of D(). Item (ii) comes from investigating a tail-weighted dependence measure that is related to an estimate of the extremal index for multivariate extreme value data. The estimation methods for extreme joint tail probabilities consist of (a) likelihood-based threshold methods (for observations of appropriate maxima/minima that lie beyond a threshold, or (b) weighted regression methods. Examples will be used for illustration of the main ideas.

 

 

Dates - January

Monday, 8.01.2018 - 1.Speech

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Place: Room 2.02.01, Parkring 11, 85748 Garching-Hochbrück

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Dates - February

Monday, 5.02.2018 - 1.Speech

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Place: Room 2.02.01, Parkring 11, 85748 Garching-Hochbrück

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