Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …

The level of modeling sophistication in financial services has increased considerably over
the years. Nowadays, the complexity of financial problems and the vast amount of data …

Investigating Bitcoin price forecasting has attracted academic attention recently. However,
despite some studies on potential economic determinants of Bitcoin price, a consensus on …

This paper addresses the interpretability problem of non-parametric option pricing models
by using the explainable artificial intelligence (XAI) approach. We study call options written …

In this paper, we start from the no-arbitrage constraints in option pricing and develop a novel
hybrid gated neural network (hGNN) based option valuation model. We adopt a …

We study the empirical hedging performance of alternative VIX option pricing models.
Recent advances in the literature find evidence of asymmetric volatility-of-volatility (similar to …

In this article, compact finite difference approximations for first and second derivatives on the
non-uniform grid are discussed. The construction of diffusion wavelets using compact finite …

Traditional options pricing relies on underlying asset volatility and contract properties.
However, asset volatility is affected by the “lead–lag effects,” known as the “momentum …

Estimating option prices and implied volatilities are critical for option risk management and
trading. Common strategies in previous studies have relied on parametric models, including …

This paper presents the numerical solution of the Black–Scholes partial differential equation
(PDE) for the evaluation of European call and put options. The proposed method is based …