On Calibration Neural Networks for extracting implied information from American options

• URL: http://arxiv.org/abs/2001.11786v1
• Date: Fri, 31 Jan 2020 12:10:24 GMT
• Title: On Calibration Neural Networks for extracting implied information from American options
• Authors: Shuaiqiang Liu, \'Alvaro Leitao, Anastasia Borovykh, Cornelis W. Oosterlee
• Abstract summary: We will employ a data-driven machine learning approach to estimate the Black-Scholes implied volatility and the dividend yield for American options. For the implied dividend yield, we formulate the inverse problem as a calibration problem and simultaneously determine implied volatility and dividend yield. It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options.
• Score: 1.911678487931003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational costs needed to solve the corresponding mathematical problem many thousands of times. We will employ a data-driven machine learning approach to estimate the Black-Scholes implied volatility and the dividend yield for American options in a fast and robust way. To determine the implied volatility, the inverse function is approximated by an artificial neural network on the computational domain of interest, which decouples the offline (training) and online (prediction) phases and thus eliminates the need for an iterative process. For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield. For this, a generic and robust calibration framework, the Calibration Neural Network (CaNN), is introduced to estimate multiple parameters. It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options.

Related papers

• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity [1.998862666797032]
  This study presents a comprehensive framework for American option pricing consisting of six interrelated modules. The framework combines nonlinear optimization algorithms, analytical and numerical models, and neural networks to improve pricing performance. The proposed model shows superior performance in pricing deep out-of-the-money options.
  arXiv  Detail & Related papers  (2024-09-26T17:50:12Z)
• Switchable Decision: Dynamic Neural Generation Networks [98.61113699324429]
  We propose a switchable decision to accelerate inference by dynamically assigning resources for each data instance. Our method benefits from less cost during inference while keeping the same accuracy.
  arXiv  Detail & Related papers  (2024-05-07T17:44:54Z)
• Improved model-free bounds for multi-asset options using option-implied information and deep learning [0.0]
  We consider the computation of model-free bounds for multi-asset options in a setting that combines dependence uncertainty with additional information on the dependence structure. We provide a fundamental theorem of asset pricing in this setting, as well as a superhedging duality that allows to transform the problem over probability measures. The latter is solved using a penalization approach combined with a deep learning approximation using artificial neural networks.
  arXiv  Detail & Related papers  (2024-04-02T22:37:22Z)
• A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO [0.0]
  Price prediction algorithms propose prices for every product or service according to market trends, projected demand, and other characteristics. As the dependent variable in price prediction, it is affected by several independent and correlated variables. This study introduces a novel decision level fusion approach to select informative variables in price prediction.
  arXiv  Detail & Related papers  (2024-03-29T07:59:33Z)
• American Option Pricing using Self-Attention GRU and Shapley Value Interpretation [0.0]
  We propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU.
  arXiv  Detail & Related papers  (2023-10-19T06:05:46Z)
• Machine learning for option pricing: an empirical investigation of network architectures [0.0]
  We investigate whether and how the choice of network architecture affects the accuracy and training time of a machine learning algorithm. For option pricing problems, where we focus on the Black--Scholes and the Heston model, the generalized highway network architecture outperforms all other variants.
  arXiv  Detail & Related papers  (2023-07-14T23:27:43Z)
• VCNet: A self-explaining model for realistic counterfactual generation [52.77024349608834]
  Counterfactual explanation is a class of methods to make local explanations of machine learning decisions. We present VCNet-Variational Counter Net, a model architecture that combines a predictor and a counterfactual generator. We show that VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem.
  arXiv  Detail & Related papers  (2022-12-21T08:45:32Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)
• Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration [118.26862029820447]
  We introduce a new notion -- emphdecision calibration -- that requires the predicted distribution and true distribution to be indistinguishable'' to a set of downstream decision-makers. Decision calibration improves decision-making on skin lesions and ImageNet classification with modern neural network.
  arXiv  Detail & Related papers  (2021-07-12T20:17:28Z)
• The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget [164.65771897804404]
  In many applications, it is desirable to extract only the relevant information from complex input data. The information bottleneck method formalizes this as an information-theoretic optimization problem. We propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information.
  arXiv  Detail & Related papers  (2020-04-24T18:29:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.