Apr

18

 The connections between sunspot activity and global climate change are a challenge for scientists to unravel (from impacts caused by volcanic dust and muliple physical variables). Jeff Watson has discussed this topic before so the clips from a couple of recent articles may be of interest.

1) From the Journal of Space Weather:

If you have the sense that the current solar cycle has been slow to build up, maybe it is more than just the "watched pot" failing to boil. A comparison with previous sunspot cycles shows that the current cycle is among the slowest-growing cycles characterized with good historical data. Figure 1 shows the smoothed sunspot number for the period from 2 years before the minimum to 2 years after it for the 24 numbered solar cycles (cycle 1 started in 1755; we are just now entering cycle 24). It illustrates the historically slow increase of the current cycle (shown in red) as of February 2011. Three of the four cycles with slower increases (shown in blue) were during the Dalton Minimum in the early nineteenth century. The fourth is the period leading into cycle 1. The red dots in the figure are cycle 24 monthly average sunspot numbers; these data are too recent to be adjusted by the smoothing algorithm that includes the influence of monthly averages within 6 months of the smoothed value. Also shown, at the bottom of the figure, for context, are the sunspot data for the first 23 cycles, which also identify the Dalton Minimum.

2) From Geophysical Research Letters:

Variations in the total solar irradiance (TSI) associated with solar activity have been argued to influence the Earth's climate system, in particular when solar activity deviates from the average for a substantial period. One such example is the 17th Century Maunder Minimum during which sunspot numbers were extremely low, as Earth experienced the Little Ice Age. Estimation of the TSI during that period has relied on extrapolations of correlations with sunspot numbers or even more indirectly with modulations of galactic cosmic rays. We argue that there is a minimum state of solar magnetic activity associated with a population of relatively small magnetic bipoles which persists even when sunspots are absent, and that consequently estimates of TSI for the Little Ice Age that are based on scalings with sunspot numbers are generally too low. The minimal solar activity, which measurements show to be frequently observable between active-region decay products regardless of the phase of the sunspot cycle, was approached globally after an unusually long lull in sunspot activity in 2008–2009. Therefore, the best estimate of magnetic activity, and presumably TSI, for the least-active Maunder Minimum phases appears to be provided by direct measurement in 2008–2009. The implied marginally significant decrease in TSI during the least active phases of the Maunder Minimum by 140 to 360 ppm relative to 1996 suggests that drivers other than TSI dominate Earth's long-term climate change.

Mar

8

A while back Sam Humbert wrote a comment on our Performance-Analytics package for R. We have since cleaned up a few of the issues noted in the preview versions of the code. Here’s the new release announcement from R-SIG-Finance:

Description: Library of econometric functions for performance and risk analysis of financial portfolios. This library aims to aid practitioners and researchers in using the latest research in analysis of both normal and non-normal return streams.

We created this library to include functionality that has been appearing in the academic literature on performance analysis and risk over the past several years, but had no functional equivalent in R. In doing so, we also found it valuable to have wrapper functions for functionality easily replicated in R, so that we could access that functionality using a function with defaults and naming consistent with common usage in the finance literature. The package covers Performance Analysis, Risk Analysis (with a separate treatment of VaR), Summary Tables of related statistics, Charts and Graphs, a variety of Wrappers and Utility functions, and some thoughts on work yet to be done.This R package contains over 80 original functions, and several additional wrappers to simplify parameters or provide accessible names for other functionality.

I suggest that you start with the summary documentation, either via the Performance-Analytics package pdf. attached to the original announcement linked above, or via the HTML version of the same.

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