Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Hientzsch, Bernhard"'
Autor:
Routray, Hardik, Hientzsch, Bernhard
We propose a simple methodology to approximate functions with given asymptotic behavior by specifically constructed terms and an unconstrained deep neural network (DNN). The methodology we describe extends to various asymptotic behaviors and multiple
Externí odkaz:
http://arxiv.org/abs/2411.05257
In [1], we calibrated a one-factor Cheyette SLV model with a local volatility that is linear in the benchmark forward rate and an uncorrelated CIR stochastic variance to 3M caplets of various maturities. While caplet smiles for many maturities could
Externí odkaz:
http://arxiv.org/abs/2408.11257
Autor:
Ogetbil, Orcan, Hientzsch, Bernhard
We formulate a forward inflation index model with multi-factor volatility structure featuring a parametric form that allows calibration to correlations between indices of different tenors observed in the market. Assuming the nominal interest rate fol
Externí odkaz:
http://arxiv.org/abs/2405.05101
Autor:
Fathi, Ali, Hientzsch, Bernhard
We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare their performance for a European call option in the presence of trans
Externí odkaz:
http://arxiv.org/abs/2302.07996
Differential machine learning (DML) is a recently proposed technique that uses samplewise state derivatives to regularize least square fits to learn conditional expectations of functionals of stochastic processes as functions of state variables. Expl
Externí odkaz:
http://arxiv.org/abs/2302.06682
Autor:
Ogetbil, Orcan, Hientzsch, Bernhard
We propose a non-parametric extension with leverage functions to the Andersen commodity curve model. We calibrate this model to market data for WTI and NG including option skew at the standard maturities. While the model can be calibrated by an analy
Externí odkaz:
http://arxiv.org/abs/2212.07972
Autor:
Ganesan, Narayan, Hientzsch, Bernhard
Predicting future values at risk (fVaR) is an important problem in finance. They arise in the modelling of future initial margin requirements for counterparty credit risk and future market risk VaR. One is also interested in derived quantities such a
Externí odkaz:
http://arxiv.org/abs/2104.11768
Publikováno v:
International Journal of Theoretical and Applied Finance, 25(02):2250011, 2022
We propose Monte Carlo calibration algorithms for three models: local volatility with stochastic interest rates, stochastic local volatility with deterministic interest rates, and finally stochastic local volatility with stochastic interest rates. Fo
Externí odkaz:
http://arxiv.org/abs/2009.14764
In this paper, we present a backward deep BSDE method applied to Forward Backward Stochastic Differential Equations (FBSDE) with given terminal condition at maturity that time-steps the BSDE backwards. We present an application of this method to a no
Externí odkaz:
http://arxiv.org/abs/2006.07635
Publikováno v:
Journal of Computational Finance, 25(4):1-25 (2022)
This paper presents a novel and direct approach to price boundary and final-value problems, corresponding to barrier options, using forward deep learning to solve forward-backward stochastic differential equations (FBSDEs). Barrier instruments are in
Externí odkaz:
http://arxiv.org/abs/2005.10966