Robust valuation and risk measurement under model uncertainty
Yuhong Xu

TL;DR
This paper develops a robust framework for valuation and risk measurement in financial markets with mean-volatility uncertainty using Peng's G-stochastic calculus, deriving model-independent pricing bounds and arbitrage conditions.
Contribution
It introduces a G-stochastic calculus-based model for markets with mean and volatility uncertainty, deriving model-independent option pricing bounds and arbitrage definitions.
Findings
Derived upper option prices using G-Brownian motion.
Established arbitrage conditions under model uncertainty.
Interpreted the super-hedging term as maximum profit/loss.
Abstract
Model uncertainty is a type of inevitable financial risk. Mistakes on the choice of pricing model may cause great financial losses. In this paper we investigate financial markets with mean-volatility uncertainty. Models for stock markets and option markets with uncertain prior distribution are established by Peng's G-stochastic calculus. The process of stock price is described by generalized geometric G-Brownian motion in which the mean uncertainty may move together with or regardless of the volatility uncertainty. On the hedging market, the upper price of an (exotic) option is derived following the Black-Scholes-Barenblatt equation. It is interesting that the corresponding Barenblatt equation does not depend on the risk preference of investors and the mean-uncertainty of underlying stocks. Hence under some appropriate sublinear expectation, neither the risk preference of investors nor…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and financial applications · Financial Risk and Volatility Modeling · Complex Systems and Time Series Analysis
