Lévy Processes, Jump Models, and Option Pricing
Summary
The answer introduces Lévy processes as a way to model jumps in asset prices and connects them to risk-neutral pricing. It contrasts two constructions of price dynamics in the Black–Scholes setting, then describes generalizing them by replacing Brownian motion with a Lévy process. The resulting exponential Lévy models can represent positive prices with jumps and provide a framework for pricing derivatives under a risk-neutral measure.
A practical motivation is that Lévy processes have tractable characteristic functions, which enable Fourier transform methods for option pricing. The response points readers toward work on measure changes, martingale measures, arbitrage and completeness, and jump-based representations relevant to hedging. These are conceptual directions rather than a worked pricing example; model selection, parameter calibration, and the choice of risk-neutral dynamics are left open.
Key ideas
- Lévy processes extend Brownian price models by allowing jumps.
- Exponential Lévy constructions can model positive asset prices under risk-neutral dynamics.
- Characteristic functions of Lévy models support Fourier methods for option pricing.
- Jump models raise questions about equivalent martingale measures, arbitrage, completeness, and hedging.
- The document gives conceptual references but no calibration or worked pricing procedure.
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# Levy process and random measure
# Levy process and random measure
I am wondering if random measures are used under a Levy process and how this connects to finance (particularly pricing). Any paper or books for suggestions is welcomed.
## Answer by skoestlmeier (score 5)
https://quant.stackexchange.com/a/43069
There is a whole literature on risk-neutral modeling with Levy processes.
Consider an arbitrage-free market where asset prices are modeled by a stochastic process $(S_t)_{t \in [0,T]}, \mathcal{F}_t$ represents the history of the asset $S$ and $\hat{S}_t=e^{-rt}S_T$ the stochastic discounted value of the asset. The discounted expectation of the terminal payoffs under $Q$ is
$$\hat{S}_t = \operatorname{E}^{Q}[\hat{S}_T|\mathcal{F}_t]$$
There are two ways to define the risk neutral dynamics in the Black-Scholes model using a Brownian motion with drift:
- Taking the exponential, i.e. $S_t=S_0 e^{B_t^0}$, where $B_t^0=(r-\sigma^2/2)t + \sigma W_t$, which is a brownian motion with drift.
- Taking the stochastic exponential by applying Ito formula, i.e. $\frac{dS_t}{S_t}=rdt + \sigma dW_t=dB_t^1$, where $B_t^1=rt+\sigma W_t$.
Levy-processes are often used for modeling jump-processes (see Cox/Ross(1976) or Merton(1976)), especially in jump-diffusion models.
In the formulas above, we can generalize the Black-Scholes model to account for jumps, by replacing the brownian motion with drift by a Levy-process. Therefore, we get
- $S_t = S_0 e^{rt + X_t}$, which is an exponential-Levy model as $(X_t)_{t \in [0,T]}$ describes a Levy-process.
- Replace $B_t^1$ by a Levy-process $Z_t$ results in $dS_t=rS_tdt + S_tdZ_t$.
Furthermore, exponential-Levy models offer analytically tractable examples of positive jump processes. The availability of closed-form expressions for characteristic function of Levy processes also allows to use Fourier transform methods for option pricing.
I recommend the following literature on Levy-processes and their use in finance:
- Measure transformations for Levy-processes are discussed in Sato(1999)
- More general martingale measures for processes with independent increments are discussed in Grandits(1999).
- The absence of arbitrage and completeness for models with jumps is discussed in Bardhan(1999).
- Predictable representations for Levy processes in terms of a sequence of jump martingales were introduced by Nualart/Schoutens(2000) and Nualart/Schoutens(2001). A financial interpretation of their results in terms of hedging with vanilla options is given by Balland(2002).
Reference:
Cont/Tankov (2004), Financial Modelling With Jump Processes, Chapman & Hall/CRC Financial Mathematics SeriesShown 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.