Implied Probability Density Function of Stock Returns
18 Jan 2024 · 18 minsDive into the intricate world of options pricing where we elegantly derive the distribution of stock moves as implied by option prices. This post is essential for options traders, as it contrasts personal views against the odds the market has already priced in. We explore risk-neutral probabilities, dissecting how option prices reflect pseudo-probabilities and delve into the mathematical details of deriving the probability density function $\phi$ and cumulative distribution function $\Phi$ from the options market. $$\begin{aligned} C(S, t, K, T) &= e^{-r\tau} \left[\int_K^\infty S_T \, \phi(S, t, S_T, T) \, dS_T - \int_K^\infty K \, \phi(S, t, S_T, T) \, dS_T\right] \\ \implies \frac{\partial C}{\partial K} &= -e^{-r \tau} \int_K^\infty \phi(S, t, S_T, T) \, dS_T \\ &= -e^{-r \tau} \Phi(S, t, K, T) \end{aligned}$$ Additionally, we enhanced this post with practical Python code snippets, including a section on using Gaussian filters and cubic interpolation to create smooth distributions from discrete market data. Ultimately, the post reveals intriguing insights into market sentiment, like the skewed nature of the PDF and briefly dive into betting in different parts of the distribution.
[Read Full Post]