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Using Itô’s Lemma to Compare Asset and Derivative Risk Premia

Article Quant Q&A · Author: user2521987

Summary

The document examines whether Itô’s lemma can derive the risk premium of a derivative without assuming in advance that it shares the underlying asset’s Sharpe ratio. It applies Itô’s lemma to a value defined as the exponential of the risk-free rate times the logarithm of the asset price, under a Black–Scholes setting with drift, dividend yield, and volatility.

The answer compares the derivative’s dynamics under the real-world and risk-neutral measures. Subtracting the respective drift terms and scaling by the diffusion coefficient yields the same excess return per unit of volatility risk as for the underlying asset. This shows how the equal-risk-premium result follows from the change of measure and the assumed model. The argument is specific to the stated framework and payoff; it does not provide a general derivation for arbitrary derivatives or relax the model’s assumptions.

Key ideas

  • Itô’s lemma expresses the derivative’s drift and diffusion in terms of the underlying asset dynamics.
  • Comparing real-world and risk-neutral dynamics isolates the excess return per unit of volatility risk.
  • For the stated derivative and Black–Scholes assumptions, this quantity matches the underlying asset’s.
  • The result depends on the model setup and should not be treated as a proof for all derivatives.

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Full text
# Is it possible to approach finding the risk premium of this derivative using Ito's Lemma?


# Is it possible to approach finding the risk premium of this derivative using Ito's Lemma?












I understand the author's intended solution to the below problem, but I thought I would see if I could solve this using first principles and Ito's Lemma instead for practice.

Let $V(S(t), t) = e^{rt}\ln{[S(t)]}$. Then

\begin{align*}V_S &= \frac{e^{rt}}{S(t)},\\ V_{SS} &= \frac{-e^{rt}}{[S(t)]^2} \text{, and}\\ V_t &= re^{rt}\ln[S(t)].\end{align*}

Assuming the Black-Scholes framework, $dS(t) = (\alpha - \delta)S(t) dt + \sigma S(t) dZ(t)$. By Ito's Lemma,

\begin{align*}dV &= (\alpha - \delta)e^{rt} dt +e^{rt}\sigma dZ(t) - \frac{1}{2}e^{rt}\sigma^2 dt + re^{rt}\ln[S(t)]dt\\ &=[(\alpha - \delta)e^{rt} - 0.5e^{rt}\sigma^2 + re^{rt}\ln[S(t)]dt + e^{rt}\sigma dZ(t).\end{align*}

It would appear that without knowing anything about $\delta$ or $\sigma$, we have no where to go. So is there not a way to solve this problem from first principles without knowing that since the Sharpe ratios of the asset and the derivative are perfectly (positively) correlated, they are equal? I.e.,

$$\frac{\gamma_V - r}{\sigma_V} = \frac{\gamma - r}{\Omega_V\sigma} = \frac{\alpha - r}{\sigma},$$

where $\gamma_V$ is the continuously compounded return on the derivative and $\Omega_V$ is the elasticity of the derivative.

## Answer by Quantuple (score 2, accepted)

https://quant.stackexchange.com/a/32871

Assume that under the real world measure $$ dS_t/S_t = (\alpha-\delta) dt + \sigma dZ_t^\Bbb{P} \tag{1} $$ Under the EMM $\Bbb{Q}$ one then needs to have (fundamental theorem of asset pricing: in the absence of arbitrage the discounted value of any self-financing portfolio should be a martingale): $$ dS_t/S_t = (r-\delta) dt + \sigma dZ_t^\Bbb{Q} \tag{2} $$ Examining $(1)$ and $(2)$ the (instantaneous) excess return per unit of volatility risk is therefore: $$ \pi_S = \frac{\alpha-\delta}{\sigma} - \frac{r-\delta}{\sigma} := \frac{\alpha-r}{\sigma} $$

Now assume $V(t,S_t) = e^{rt} \ln(S_t)$. As you noted, applying Itô's lemma yields: $$ dV_t = e^{rt}\left[ r\ln(S_t) dt + \frac{dS_t}{S_t} - \frac{1}{2} \sigma^2 dt \right] \tag{3} $$ Writing $dV_t$ under real world and risk-neutral measures respectively plugging $(1)$ and $(2)$ then gives: $$ dV_t = e^{rt}\left[ (\alpha-\delta) + r\ln(S_t) - \frac{1}{2} \sigma^2 \right] dt + e^{rt} \sigma dZ_t^\Bbb{P} \tag{4} $$ $$ dV_t = e^{rt}\left[ (r-\delta) + r\ln(S_t) - \frac{1}{2} \sigma^2 \right] dt + e^{rt} \sigma dZ_t^\Bbb{Q} \tag{5} $$ The (instantaneous) excess return per unit of volatility risk is now: $$ \pi_V = \frac{e^{rt}\left((\alpha-\delta) + r\ln(S_t) - \frac{1}{2} \sigma^2 \right)}{e^{rt}\sigma} - \frac{e^{rt}\left( (r-\delta) + r\ln(S_t) - \frac{1}{2} \sigma^2 \right)}{e^{rt}\sigma} := \frac{\alpha-r}{\sigma} = \pi_S $$ So you see that you fall back on the result that the Sharpe ratios are the same without any prior knowledge.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.