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

 

 

Dates - Mai

Monday, 07.05.2018 - 1. Speech

Speaker: Prof. Michael Ludkowski

Topic: Marrying Stochastic Control and Machine Learning: from Bermudan Options to Natural Gas Storage and Microgrids

Time: 14:15 o'clock

Place: B 349, Theresienstr. 39, 80333 Munich

Description: Simulation-based strategies bring the machine learning toolbox to numerical resolution of stochastic control models. I will begin by reviewing the history of this idea, starting with the seminal work by Longstaff-Schwartz and through the popularized Regression Monte Carlo framework. I will then describe the Dynamic Emulation Algorithm (DEA) that we developed, which unifies the different existing approaches in a single modular template and emphasizes the two central aspects of regression architecture and experimental design. Among novel DEA implementations, I will discuss Gaussian process regression, as well as numerous simulation designs (space-filling, sequential, adaptive, batched). The overall DEA template is illustrated with multiple examples drawing from Bermudan option pricing, natural gas storage valuation, and optimal control of back-up generator in a power microgrid. This is partly joint work with Aditya Maheshwari (UCSB).

Monday, 07.05.2018 - 2. Speech

Speaker: Dr. Tobias Kley

Topic: Quantile-Based Spectral Analysis of Time Series

Time: 15:00 o'clock

Place: B 349, Theresienstr. 39, 80333 Munich

Description: Classical methods for the spectral analysis of time series account for covariance-related serial dependencies. This talk will begin with a brief introduction to these traditional procedures. Then, an alternative method is presented, where, instead of covariances, differences of copulas of pairs of observations and the independence copula are used to quantify serial dependencies. The Fourier transformation of these copulas is considered and used to define quantile-based spectral quantities. They allow to separate marginal and serial aspects of a time series and intrinsically provide more information about the conditional distribution than the classical location-scale model. Thus, quantile-based spectral analysis is more informative than the traditional spectral analysis based on covariances. For an observed time series the new spectral quantities are then estimated. The asymptotic properties, including the order of the bias and process convergence, of the estimator (a function of two quantile levels) are established. The results are applicable without restrictive distributional assumptions such as the existence of finite moments and only a weak form of mixing, such as alpha-mixing, is required.

Monday, 07.05.2018 - 3. Speech

Speaker: Dr. Gregor Kastner

Topic: Bayesian Time-Varying Covariance Estimation in Many Dimensions using Sparse Factor Stochastic Volatility Models

Time: 16:00 o'clock

Place: B 349, Theresienstr. 39, 80333 München

Description: We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global-local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500 members. Our approach yields precise correlation estimates, strong implied minimum variance portfolio performance and superior forecasting accuracy in terms of log predictive scores when compared to typical benchmarks. Furthermore, we discuss the applicability of the method to capture conditional heteroskedasticity in large vector autoregressions.

 

 

 

 

Dates - July

Monday, 02.07.2018 - 1. Speech

Speaker: Birgit Rudolf

Topic: tba

Time: 15:00 o'clock

Place: B 349, Theresienstr. 39, 80333 Munich

Description: 

Monday, 02.07.2018 - 2. Speech

Speaker: Dr. Nils Detering

Topic: tba

Time: 16:00 o'clock

Place: B 349, Theresienstr. 39, 80333 Munich

Description: