6 papers
The Physical Crash Frontier: What Finite Option Quotes Can and Cannot Reveal
Jirong Zhuang
Physical crash probabilities recovered from option prices depend on a pricing kernel and on a risk-neutral distribution that finitely many bid and ask quotes do not identify. For a…
The Risk-Neutral Crash Frontier: Sharp Joint Bounds on Crash Probability and Conditional Depth from Option Bid-Ask Quotes
Jirong Zhuang
Index put prices are the market's quotes for crash insurance, and a put's value equals the probability of a crash times the expected shortfall given one. The market therefore price…
Meta-Learning Neural Process for Implied Volatility Surfaces with SABR-induced Priors
Jirong Zhuang, Xuan Wu
We treat implied volatility surface (IVS) reconstruction as a learning problem guided by two principles. First, we adopt a meta-learning view that trains across trading days to lea…
SABR-Informed Multitask Gaussian Process: A Synthetic-to-Real Framework for Implied Volatility Surface Construction
Jirong Zhuang, Xuan Wu
This study introduces a SABR-informed multitask Gaussian process for constructing implied volatility surfaces from sparse option quotes. We treat a dense synthetic dataset generate…
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient
Weiguo Lu, Xuan Wu, Deng Ding +3
Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative t…
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
Jirong Zhuang, Deng Ding, Weiguo Lu +2
In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR). We incorporate dee…