Paper: arXiv 2403.11897

Authors: Ofelia Bonesini, Antoine Jacquier, Aitor Muguruza

Abstract

One the one hand, rough volatility has been shown to provide a consistent framework to capture the properties of stock price dynamics both under the historical measure and for pricing purposes. On the other hand, market price of volatility risk is a well-studied object in Financial Economics, and empirical estimates show it to be stochastic rather than deterministic. Starting from a rough volatility model under the historical measure, we take up this challenge and provide an analysis of the impact of such a non-deterministic risk for pricing purposes.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper tackles a complex mathematical topic by combining rough fractional dynamics with stochastic market price of volatility risk. While the theoretical derivations are highly advanced, empirical validation is limited primarily to calibration under pricing measures rather than extensive out-of-sample backtesting.

Research Flowchart

  flowchart TD
    A[Research Goal: Model pricing impact of stochastic volatility risk premium in rough volatility] --> B[Input: Stock prices & historical rough volatility dynamics]
    B --> C[Methodology: Introduce non-deterministic market price of volatility risk]
    C --> D[Compute pricing measure via change of measure transform]
    D --> E[Outcome: Unified asset pricing framework incorporating stochastic risk premium]

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0 of 32 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.