mpv function

Realized Multipower Variation

Realized Multipower Variation

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,,XtniX^i_{t_0},X^i_{t_1},\dots,X^i_{t_n} be the observation data of the ii-th component (i.e. the ii-th component of the zoo.data of yuima).

When rr is a kk-dimensional vector of non-negative numbers, mpv(yuima,r,normalize=TRUE) is defined as the d-dimensional vector with i-th element equal to

μr[1]1μr[k]1nr[1]++r[k]21j=1nk+1ΔXtjir[1]ΔXtj+1ir[2]ΔXtj+k1ir[k], \mu_{r[1]}^{-1}\cdots\mu_{r[k]}^{-1}n^{\frac{r[1]+\cdots+r[k]}{2}-1}\sum_{j=1}^{n-k+1}|\Delta X^i_{t_{j}}|^{r[1]}|\Delta X^i_{t_{j+1}}|^{r[2]}\cdots|\Delta X^i_{t_{j+k-1}}|^{r[k]},

where μp\mu_p is the p-th absolute moment of the standard normal distribution and ΔXtji=XtjiXtj1i\Delta X^i_{t_{j}}=X^i_{t_j}-X^i_{t_{j-1}}. If normalize is FALSE the result is not multiplied by μr[1]1μr[k]1\mu_{r[1]}^{-1}\cdots\mu_{r[k]}^{-1}.

When rr 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

μr1i1μrkii1nr1i++rkii21j=1nki+1ΔXtjir1iΔXtj+1ir2iΔXtj+ki1irkii, \mu_{r^i_1}^{-1}\cdots\mu_{r^i_{k_i}}^{-1}n^{\frac{r^i_1+\cdots+r^i_{k_i}}{2}-1}\sum_{j=1}^{n-k_i+1}|\Delta X^i_{t_{j}}|^{r^i_1}|\Delta X^i_{t_{j+1}}|^{r^i_2}\cdots|\Delta X^i_{t_{j+k_i-1}}|^{r^i_{k_i}},

where r1i,,rkiir^i_1,\dots,r^i_{k_i} is the i-th component of r. If normalize is FALSE the result is not multiplied by μr1i1μrkii1\mu_{r^i_1}^{-1}\cdots\mu_{r^i_{k_i}}^{-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 1/3 mpv(yuima,1) # true value is 1/2 mpv(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)
  • Maintainer: Stefano M. Iacus
  • License: GPL-2
  • Last published: 2024-02-29