Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging

Ramazan Gençay A

169 citations0/0 files storedIEEE Transactions on Neural Networks

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stochastic processes and financial applications Neural Networks and Applications Stock Market Forecasting Methods

Abstract

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 169 citations exist upstream.

References

1994Comparing Predictive Accuracy 1991Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation 1994A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks 2000Pricing and hedging derivative securities with neural networks and a homogeneity hint

Cited by

2019Neural Networks for Option Pricing and Hedging: A Literature Review 2019Pricing Options and Computing Implied Volatilities using Neural Networks 2009Nonlinearity and intraday efficiency tests on energy futures markets 2010The Econometrics of Option Pricing 2009Option Pricing With Modular Neural Networks 2020Option pricing using Machine Learning 2014Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options 2004Black–Scholes versus artificial neural networks in pricing FTSE 100 options

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.