By Eric Zivot

This ebook represents an integration of concept, equipment, and examples utilizing the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the perform of economic econometrics. it's the first e-book to teach the facility of S-PLUS for the research of time sequence facts. it's written for researchers and practitioners within the finance undefined, educational researchers in economics and finance, and complex MBA and graduate scholars in economics and finance.

Readers are assumed to have a easy wisdom of S-PLUS and a superb grounding in uncomplicated data and time sequence innovations. This variation covers S+FinMetrics 2.0 and comprises new chapters.

**Read or Download Modeling Financial Time Series with S-PLUS® PDF**

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**Extra resources for Modeling Financial Time Series with S-PLUS®**

**Sample text**

This chapter discusses the basics of working with financial time series data in the form of S-PLUS "timeSeries" objects. It begins with a discussion of the specification of "timeSeries" and "timeDate" objects in S-PLUS and gives examples of how to specify common "timeDate" sequences for financial time series. Basic manipulations of financial time series are discussed and illustrated. These manipulations include aggregating and disaggregating time series, handling of missing values, creations of lags and differences and asset return calculations.

5) where ln{-) is the natural log function. 3 Time Series Manipulation in S-PLUS 45 Rearranging gives Pt = Pt-1ert' so that Tt is the continuously compounded growth rate in prices between periods t - 1 and t. This is to be contrasted with Rt which is the simple growth rate in prices between periods t -1 and t without any compounding. Since ln ( ~) = ln( x) - ln(y) it follows that ln(~J ln(Pt) - ln(Pt-1) Pt- Pt-1 where Pt = ln(Pt)· Hence, the continuously compounded one period return, rt, can be computed simply by taking the first difference of the natural logarithms of prices between periods t - 1 and t.

Intra-day Irregularly Spaced Sequences Sequences of irregularly spaced intra-day dates may be created using the function timeCalendar. For example, consider creating a sequence of hourly observations only during the hypothetical trading hours from 9:00 AM to 3:00 PM from Monday January 3, 2000 through Tuesday January 4, 2000. Such a sequence may be created using timeCalendar as follows > timeCalendar(h=rep(9:15,2),d=rep(3:4,each=7), + y=2000,format="%a %b %d, %Y %02I:%02M %p") [1] Mon Jan 3, 2000 09:00 AM Mon Jan 3, 2000 10:00 [3] Mon Jan 3, 2000 11:00 AMMon Jan 3, 2000 12:00 [5] Mon Jan 3, 2000 01:00 PM Mon Jan 3, 2000 02:00 [7] Mon Jan 3, 2000 03:00 PM Tue Jan 4, 2000 09:00 [9] Tue Jan 4, 2000 10:00 AM Tue Jan 4, 2000 11:00 [11] Tue Jan 4, 2000 12:00 PM Tue Jan 4, 2000 01:00 [13] Tue Jan 4, 2000 02:00 PM Tue Jan 4, 2000 03:00 AM PM PM AM AM PM PM In a similar fashion, a sequence of minute observations from 9:00 AM to 3:00 PM on Monday January 3, 2000 and Tuesday January 4, 2000 may be created using > timeCalendar(min=rep(rep(0:59,6),2), 28 2.