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Found 16 papers, 2 papers with code
A novel scaling approach for unbiased adjustment of risk estimators
The assessment of risk based on historical data faces many challenges, in particular due to the limited amount of available data, lack of stationarity, and heavy tails.
Robust asymptotic insurance-finance arbitrage
In this setting, we describe conditional dependence by means of copulas and illustrate how the ${Q}\mathscr{P}$-evaluation can be used for the pricing of hybrid insurance products.
Classical and deep pricing for Path-dependent options in non-linear generalized affine models
In this work we consider one-dimensional generalized affine processes under the paradigm of Knightian uncertainty (so-called non-linear generalized affine models).
Estimating value at risk: LSTM vs. GARCH
Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task.
Term structure modelling with overnight rates beyond stochastic continuity
Overnight rates, such as the SOFR (Secured Overnight Financing Rate) in the US, are central to the current reform of interest rate benchmarks.
Estimating and backtesting risk under heavy tails
While the estimation of risk is an important question in the daily business of banking and insurance, many existing plug-in estimation procedures suffer from an unnecessary bias.
Robust deep hedging
We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases.
Defaultable term structures driven by semimartingales
We consider a market with a term structure of credit risky bonds in the single-name case.
A conditional version of the second fundamental theorem of asset pricing in discrete time
We consider a financial market in discrete time and study pricing and hedging conditional on the information available up to an arbitrary point in time.
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
Inspired by recent advances that allow to combine deep learning with dynamic modeling, we investigate whether such approaches can be useful for uncovering complex structure, in particular for an extreme small data setting with only two observations time points for each individual.
Estimating and backtesting risk under heavy tails
While the {estimation} of risk is an important question in the daily business of banking and insurance, many existing plug-in estimation procedures suffer from an unnecessary bias.
Insurance-Finance Arbitrage
Most insurance contracts are inherently linked to financial markets, be it via interest rates, or -- as hybrid products like equity-linked life insurance and variable annuities -- directly to stocks or indices.
Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework
We find very good results for the single curve markets and many challenges for the multi curve markets in a Vasicek framework.
Generalized statistical arbitrage concepts and related gain strategies
Generalized statistical arbitrage concepts are introduced corresponding to trading strategies which yield positive gains on average in a class of scenarios rather than almost surely.
Fair Estimation of Capital Risk Allocation
In this paper we develop a novel methodology for estimation of risk capital allocation.
Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF)
With volumetric data from widefield fluorescence microscopy, many emerging questions in biological and biomedical research are being investigated.
