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Assume a constant risk-free rate $r$ and no dividends. Generalisation is straightforward. To preclude arbitrage opportunities, under the risk-neutral measure $\Bbb{Q}$, the discounted asset price process should be a $\Bbb{Q}$-martingale i.e. $$S_0 = \Bbb{E}^\Bbb{Q}_0 \left[ e^{-rt} S_t \right] \iff \Bbb{E}^\Bbb{Q}_0 \left[ S_t \right] = S_0 \exp(rt) \... 7 Given for all i the mean of \epsilon_i is k and that the \{\epsilon_i\}_i are i.i.d., we have^{\text{(1)}}:$$\begin{align} E\left[\prod_{i=1}^{N_t}(1+\epsilon_i)\right] &=E\left[E\left[\prod_{i=1}^{N_t}(1+\epsilon_i)|N_t\right]\right] \\[6pt] &=E\left[\prod_{i=1}^{N_t}E\left[(1+\epsilon_i)|N_t\right]\right] \\[6pt] &=E\left[\prod_{i=1}...

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We assume that the process $\{J_t, \, t\ge 0\}$ is defined at the jump times of the Poisson process $\{N_t, \, t \ge 0\}$, and all the jump sizes are independent and identically distributed. That is, \begin{align*} Q_t \equiv \int_0^t (J_t-1) dN_t = \sum_{n=1}^{N_t} (J_i-1), \end{align*} where $J_i$, for $i=1, \ldots, \infty$, are independent and $\xi_i = \... 5 Intuitively, cadlag expresses the fact that we know a jump has occurred after the fact, but we never have advance knowledge that the jump is about to occur (i.e no knowledge of the starting point for the jump or that a jump is "under way"). Each jump is a surprise, after which we believe there will be no jumps at least for a little while. I hear it in the ... 5 Answer Assuming the Poisson process$N_t$is independent from the Brownian motions$(W_{1,t},W_{2,t}), you'll have \begin{align} df(X_{1,t},X_{2,t}) &= \frac{\partial f}{\partial X_{1,t}} dX_{1,t}^c + \frac{\partial f}{\partial X_{2,t}} dX_{2,t}^c + \dots \\ &+ \frac{1}{2} \frac{\partial^2 f}{\partial X_{1,t}^2 } d\langle X_{1,t} \rangle_t^c + \... 4 The book Stochastic calculus for finance by Steven Shreve gives a good introduction to stochastic calculus applied to finance. A whole chapter is dedicated to the Itô Integral for example. It covers a large spectrum ranging from probability theory to stochastic financial models. I strongly recommend it! 4 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$the stochastic discounted value of the asset. The discounted expectation of the terminal ... 4 Elementary Stochastic Calculus by Thomas Mikosch is an excellent introduction to the topic in a very compact way. Alternatively, Stochastic Calculus for Finance II: Continuous-Time Models by Steven Shreve is a more comprehensive reference which is very much oriented to applications in finance. 3 For diffusion models (i.e. no jumps): Local volatility models: match vanilla options market prices; give unrealistic volatility dynamic (smile flattens when we move forward in time); Stochastic volatility models: don't match vanilla options market prices (not enough skew for short dated expiries); give realistic volatility dynamics. You might want to ... 3 Jumps are an attempt to solve a math mistake in Modern Portfolio Theory. In the 19502-70s, economists were working on solving the variance-mean tradeoff. Furthermore, they needed to do so with punchcard computing. That radically restricted the set of computable, potential solutions. Both the normal distribution and the log-normal distribution are ... 2 No arbitrage means that you can't have a portfolio with a positive expectation without risk. let's suppose that the value of option with jumps is lower than$C_{BS}(0,S_0)$Please consider the following portfolio at time 0: Sell BS hedge on option with jumps with price$C_{BS}(0,S_0)$. Buy option with jumps with price$P_J(0,S_0)$. Keep the rest of money$...

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If you're interested in learning about stochastic calculus outside of the context of quant finance (which I think is a better approach than learning about it solely in the context of finance), check out Stochastic Integration and Differential Equations by Protter.

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Let $P(t, r_t, T)$ be the bond price at time $t$, where $0 \leq t \leq T$. Then, by Ito's formula, \begin{align*} &\ P(t, r_t, T) \\ =& P(0, r_0, T) + \int_0^t\partial_s P(s, r_s, T) ds + \int_0^t\partial_r P(s, r_{s-}, T) dr_s + \frac{1}{2}\sigma^2 \int_0^t r_s\partial_{rr} P(s, r_s, T)ds\\ & \quad +\sum_{s \leq t}\big[P(s, r_s, T) - P(s, r_{s-}...

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Consider “Paul Wilmott Introduces Quantitative Finance” if you look for an enjoyable read, good intuition and a not too academic approach

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Jumps do not imply fat tails. See the simulation in R. Note that the excess kurtosis of [normal variable + jump] is negative. > set.seed(1) > Normal_Variable <- rnorm(1e8) > kurtosis(Normal_Variable) [1] -0.000628316 > Jump <- 2 * ((runif(1e8) < 0.5) * 2 - 1) > kurtosis(Normal_Variable + Jump) [1] -1.280009

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Actually, I do not think it's true. Jumps, when added to the Black-Scholes (BS) dynamics, do modify the volatility surface. However, the volatility skew may get inverted: the implied BS volatility may be higher when the strike is closer to the current value $S(0)$ of the underlying asset $S$. Consider an idealized example:  \log(S(t+dt) / S(t)) ={\rm[...

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Defining $\tilde{S}_n = S_n/S_{n-1}$ (which is well defined, assuming $S_n > 0$ for all $n$), the problem becomes that of barrier option pricing. In particular, you're looking to price a down-and-out barrier option. I wrote my dissertation on barrier options a couple of years ago. You might be able to find some inspiration there. You can find it on ...

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I don't think you can correlate discrete processes in the traditional sense. Instead, I would make the two Poisson intensities time-varying through which a degree of "jump similarity" can be injected Say each jump intensity is a positive mean reverting process, such as an exponential OU, where the increments are jointly distributed (I.e. with correlation) ...

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TLDR: The jump frequency depends on how you specify the jump size distribution. If you want the $\lambda$ to actually represent the jump frequency under a certain jump-diffusion model, then you should jointly estimate all model parameters, e.g. using maximum likelihood estimation (MLE) or generalized method of moments (GMM). Example: Consider a general ...

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While you are asking about the call price curve, the effect of adding compound Poisson jumps to a diffusion is more clearly observable when looking at either the implied probability density or the implied volatility smile. We can also prove that the excess kurtosis of the logarithmic returns is always non-negative. Excess Kurtosis is Non-Negative First, we ...

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If I understood well, your model falls into the generic case of affine models. This reference might help you : http://arxiv.org/pdf/1512.03677v1.pdf

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