Basket Options with Volatility Skew: Calibrating a Local Volatility Model by Sample Rearrangement
ArXiv ID: 2407.02901 “View on arXiv”
Authors: Unknown
Abstract
The pricing of derivatives tied to baskets of assets demands a sophisticated framework that aligns with the available market information to capture the intricate non-linear dependency structure among the assets. We describe the dynamics of the multivariate process of constituents with a copula model and propose an efficient method to extract the dependency structure from the market. The proposed method generates coherent sets of samples of the constituents process through systematic sampling rearrangement. These samples are then utilized to calibrate a local volatility model (LVM) of the basket process, which is used to price basket derivatives. We show that the method is capable of efficiently pricing basket options based on a large number of basket constituents, accomplishing the calibration process within a matter of seconds, and achieving near-perfect calibration to the index options of the market.
Keywords: Basket derivatives, Copula models, Local volatility model, Derivatives pricing, Dependency structure, Derivatives
Complexity vs Empirical Score
- Math Complexity: 8.5/10
- Empirical Rigor: 3.0/10
- Quadrant: Lab Rats
- Why: The paper involves advanced mathematics including copulas, local volatility models, Dupire’s formula, and rearrangement algorithms with heavy LaTeX derivations. It lacks empirical validation such as backtests, code, or statistical metrics, focusing instead on theoretical calibration methods and numerical approximations.
flowchart TD
A["Research Goal: Efficient Pricing of Basket Options with Volatility Skew"] --> B["Input: Market Data on Basket Constituents"]
B --> C["Methodology: Construct Samples via Copula-Based Rearrangement"]
C --> D["Computational Process: Calibrate Local Volatility Model LVM"]
D --> E["Outcome: Fast Pricing of Basket Derivatives"]
E --> F["Key Finding: Near-Perfect Calibration to Index Options"]