Description Author(s) References See Also Examples

A new Poisson subordinated distribution is proposed to capture major leptokurtic features in log-return time series of financial data. This distribution is intuitive, easy to calculate, and converge quickly. It fits well to the historical daily log-return distributions of currencies, commodities, Treasury yields, VIX, and, most difficult of all, DJIA. It serves as a viable alternative to the more sophisticated truncated stable distribution.

Stephen Horng-Twu Lihn <[email protected]>

On a Poisson Subordinated Distribution for Precise Statistical Measurement of Leptokurtic Financial Data, SSRN 2032762, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2032762.

`dji_logr`

,
`rawmean`

,
`rawdensity`

,
`LIHNPSD_prepare_data`

,
`LIHNPSD_theoretical_result`

,
`LIHNPSD_plot_std4gr`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# Load the daily log-return data of DJIA
data(dji_logr)
# Construct the S3 object for PSD
dist <- list( sigma= 0.004625, alpha= 0.292645, gamma= 0.482744, beta= -0.154049, location= 0.002968 )
class(dist) <- "LIHNPSD"
dist <- rawmean(dist)
# A simple graph of the distribution's log PDF
x <- seq(-0.1,0.1,by=0.1/1000)
plot( x, log(rawdensity(dist,x)), pch=".")
# The more sophisticated fit and graphs
dt <- LIHNPSD_prepare_data(dji_logr, breaks=160, merge_tails=c(4,2))
th <- LIHNPSD_theoretical_result(dist, dt)
LIHNPSD_plot_std4gr(th, dt)
``` |

LIHNPSD documentation built on May 29, 2017, 11:25 p.m.

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