pdshare: Computes information share & component share weights
In ifrogs: Finance routines developed by the IGIDR Finance Research Group.

Description Usage Arguments Details Value Author(s) References See Also Examples

This function implements the two most commonly used techniques to measure price discovery in multiple markets. These are information share (Hasbrouck (1995)) and the component share approach (Booth et al. (1999), Chu et al. (1999), Harris et al. (2002)) which utilises the Gonzalo and Granger (1995) permanent-transitory decomposition.

1	pdshare(x, override.lags = NULL, lag.max = 10)

`x`	Is a numeric matrix / data frame which has two columns with log prices of two markets.
`override.lags`	Is an integer that specifies user-defined lags to be used for VECM estimation. Uses NULL as default. See Details.
`lag.max`	Is an integer that specifies the maximum lag order to be used for selecting the number of lags in VARselect. Is used when ‘override.lags’ is NULL. Uses 10 as default.

The function estimates the information share (IS) and component share weights for a two market case. This is done by first estimating the VECM model for two cointegrated price series using the functionality in package ‘urca’. The subsequent VAR and VMA representation are obtained using package ‘vars’.

The number of lags to be used for VECM estimation can be pre-specified by the user in the argument ‘override.lags’. The default is NULL. If the argument is kept as NULL, then the number of lags are decided based on the AIC criterion using the function ‘VARselect’ from package ‘vars’. The maximum number of lags to be used in ‘VARselect’ can be specified using the argument ‘lag.max’.

To achieve the lower and upper bound of IS using triangularization of covariance matrix, the function first computes IS for the supplied ordering. The resulting IS estimate maximises the share of Market 1 and minimises the share of Market 2. The results are saved in ‘is.original.ordering’. Subsequently, the ordering is reversed. The corresponding IS estimate maximises the share of Market 2 and minimises it for Market 1.

A list of the following five elements:

`is.original.ordering`	Information shares of Market 1 & 2 under the supplied ordering
`is.reversed.ordering`	Information shares of Market 2 & 1 under the reversed ordering
`component.share`	Component share weights of Market 1 & 2
`var.covar.matrix`	Variance covariance matrix of the residuals
`lags.used`	Number of lags used in VECM estimation

Nidhi Aggarwal nidhi@igidr.ac.in

Booth G, So R, Tse Y (1999). Price discovery in the German equity index derivatives. Journal of Futures Markets, 19(6), 619-643.

Chu QC, Hsieh WG, Tse Y (1999). Price discovery on the S&P 500 index markets: An analysis of spot index, index futures and SPDRs. International Review of Financial Analysis, 8(1), 21-34

Gonzalo J, Granger C (1995). Estimation of common long-memory components in cointegrated systems. Journal of Business and Economic Statistics, 13(1), 27-35.

Harris F, McInish TH, Wood R (2002). Security price adjustments across exchanges: An investigation of common factor components for Dow stocks. Journal of Financial Markets, 5(3), 277-308.

Hasbrouck J (1995). One security, many markets: Determining the contributions to price discovery. Journal of Finance, 50(4), 1175-1199.

VARselect

  data(is_reliance)
  head(is_reliance)      ## Two columns with data on spot and futures prices
  ln_reliance <- log(is_reliance[,-1])  ## removes the first column of datetime
                               ##  and takes log of the prices
  pdshare(ln_reliance, lag.max=120)  
  pdshare(ln_reliance, override.lags=60)