Ad
related to: quadratic variation of martingale practice questions multiple choicekutasoftware.com has been visited by 10K+ users in the past month
Search results
Results from the WOW.Com Content Network
The quadratic variation exists for all continuous finite variation processes, and is zero. This statement can be generalized to non-continuous processes. Any càdlàg finite variation process X {\displaystyle X} has quadratic variation equal to the sum of the squares of the jumps of X {\displaystyle X} .
First, the Doob–Meyer decomposition theorem is used to show that a decomposition M 2 = N + M exists, where N is a martingale and M is a right-continuous, increasing and predictable process starting at zero. This uniquely defines M , which is referred to as the predictable quadratic variation of M.
A convex function of a martingale is a submartingale, by Jensen's inequality. For example, the square of the gambler's fortune in the fair coin game is a submartingale (which also follows from the fact that X n 2 − n is a martingale). Similarly, a concave function of a martingale is a supermartingale.
The martingale representation theorem can be used to establish the existence of a hedging strategy. Suppose that ( M t ) 0 ≤ t < ∞ {\displaystyle \left(M_{t}\right)_{0\leq t<\infty }} is a Q-martingale process, whose volatility σ t {\displaystyle \sigma _{t}} is always non-zero.
A single realization of a one-dimensional Wiener process A single realization of a three-dimensional Wiener process. In mathematics, the Wiener process (or Brownian motion, due to its historical connection with the physical process of the same name) is a real-valued continuous-time stochastic process discovered by Norbert Wiener.
Visualisation of the Girsanov theorem. The left side shows a Wiener process with negative drift under a canonical measure P; on the right side each path of the process is colored according to its likelihood under the martingale measure Q. The density transformation from P to Q is given by the Girsanov theorem.
Hans Föllmer provided a non-probabilistic proof of the Itô formula and showed that it holds for all functions with finite quadratic variation. [ 3 ] Let f ∈ C 2 {\displaystyle f\in C^{2}} be a real-valued function and x : [ 0 , ∞ ] → R {\displaystyle x:[0,\infty ]\to \mathbb {R} } a right-continuous function with left limits and finite ...
Stochastic exponential of a local martingale is again a local martingale. All the formulae and properties above apply also to stochastic exponential of a complex -valued X {\displaystyle X} . This has application in the theory of conformal martingales and in the calculation of characteristic functions.
Ad
related to: quadratic variation of martingale practice questions multiple choicekutasoftware.com has been visited by 10K+ users in the past month