Implementing Risk-Averse Implied Binomial Trees: Additional Theory, Empirics, and Extensions
46 Pages Posted: 3 Jul 2005 Last revised: 14 Jun 2009
Date Written: June 11, 2009
Arnold, Crack and Schwartz (2010) generalize the Rubinstein (1994) risk-neutral implied binomial tree (R-IBT) model by introducing a risk premium. Their new risk-averse implied binomial tree model (RA-IBT) has both probabilistic and pricing applications. They use the RA-IBT model to estimate the pricing kernel (i.e., marginal rate of substitution) and implied relative risk aversion for a representative agent.
This paper presents additional theoretical details on the use of assumed utility functions to generate discount rates in the RA-IBT and theoretical details on the propagation of risk-averse probabilities through an RA-IBT (and how this process differs from the propagation of probabilities through a Rubinstein R-IBT). We also present both no-arbitrage and CAPM-driven derivations of the certainty equivalent risk-adjusted discounting formula that is used in Arnold, Crack and Schwartz (2010) and a direct estimation routine for the RA-IBT that is similar to Rubinstein’s “one-two-three” technique.
This paper also presents additional empirical applications of the model, including a comparison of risk-neutral and risk-averse implied distributions, and applications of the RA-IBT to financial options trading, time series return forecasting, and a previously infeasible corporate finance real option valuation problem. We also use the RA-IBT to explore the differences between risk-neutral and risk-averse moments of returns. We also discuss practical applications of the RA-IBT model to Value at Risk and stochastic volatility option pricing models.
Keywords: Binomial Option Pricing, Implied Binomial Trees, Physical Probabilities, Risk-Neutral Probabilities, Calibration, Representative Agent, Risk-Averse Probabilities, Hedge Funds
JEL Classification: A23, G13
Suggested Citation: Suggested Citation