The function returns the realized MultiPower Variation (mpv), defined in Barndorff-Nielsen and Shephard (2004), for each component.
mpv(yuima, r =2, normalize =TRUE)
Arguments
yuima: an object of yuima-class or yuima.data-class.
r: a vector of non-negative numbers or a list of vectors of non-negative numbers.
normalize: logical. See `Details'.
Details
Let d be the number of the components of the zoo.data of yuima.
Let Xt0i,Xt1i,…,Xtni be the observation data of the i-th component (i.e. the i-th component of the zoo.data of yuima).
When r is a k-dimensional vector of non-negative numbers, mpv(yuima,r,normalize=TRUE) is defined as the d-dimensional vector with i-th element equal to
where μp is the p-th absolute moment of the standard normal distribution and ΔXtji=Xtji−Xtj−1i. If normalize is FALSE the result is not multiplied by μr[1]−1⋯μr[k]−1.
When r is a list of vectors of non-negative numbers, mpv(yuima,r,normalize=TRUE) is defined as the d-dimensional vector with i-th element equal to
where r1i,…,rkii is the i-th component of r. If normalize is FALSE the result is not multiplied by μr1i−1⋯μrkii−1.
Returns
A numeric vector with the same length as the zoo.data of yuima
References
Barndorff-Nielsen, O. E. and Shephard, N. (2004) Power and bipower variation with stochastic volatility and jumps, Journal of Financial Econometrics, 2 , no. 1, 1--37.
Barndorff-Nielsen, O. E. , Graversen, S. E. , Jacod, J. , Podolskij M. and Shephard, N. (2006) A central limit theorem for realised power and bipower variations of continuous semimartingales, in: Kabanov, Y. , Lipster, R. , Stoyanov J. (Eds.), From Stochastic Calculus to Mathematical Finance: The Shiryaev Festschrift, Springer-Verlag, Berlin, pp. 33--68.
Author(s)
Yuta Koike with YUIMA Project Team
See Also
setData, cce, minrv, medrv
Examples
## Not run:set.seed(123)# One-dimensional case## Model: dXt=t*dWt+t*dzt, ## where zt is a compound Poisson process with intensity 5 and jump sizes distribution N(0,0.1). model <- setModel(drift=0,diffusion="t",jump.coeff="t",measure.type="CP", measure=list(intensity=5,df=list("dnorm(z,0,sqrt(0.1))")), time.variable="t")yuima.samp <- setSampling(Terminal =1, n =390)yuima <- setYuima(model = model, sampling = yuima.samp)yuima <- simulate(yuima)plot(yuima)mpv(yuima)# true value is 1/3mpv(yuima,1)# true value is 1/2mpv(yuima,rep(2/3,3))# true value is 1/3# Multi-dimensional case## Model: dXkt=t*dWk_t (k=1,2,3).diff.matrix <- diag(3)diag(diff.matrix)<- c("t","t","t")model <- setModel(drift=c(0,0,0),diffusion=diff.matrix,time.variable="t", solve.variable=c("x1","x2","x3"))yuima.samp <- setSampling(Terminal =1, n =390)yuima <- setYuima(model = model, sampling = yuima.samp)yuima <- simulate(yuima)plot(yuima)mpv(yuima,list(c(1,1),1,rep(2/3,3)))# true varue is c(1/3,1/2,1/3)## End(Not run)