estimate_hs_mod: Estimates the model of Huang & Stoll (1997) using the...
In simonsays1980/tim: Estimation of Trade Indicator Models

Description Usage Arguments Details Value References

View source: R/HS.R

Estimates the model of Huang & Stoll (1997) using the approach by Theissen & Zehnder (2015)

1 2	estimate_hs_mod(price_diff, price, indicator, indicator_lag, midquote, midquote_lag)

`price_diff`	a numeric vector containing the series of first price differences.
`price`	a numeric vector containing the price series.
`indicator`	an integer vector containing the trade direction with a buy as 1 and a sell as -1.
`indicator_lag`	an integer vector containing the first lag of the trade indicator series.
`midquote`	a numeric vector with the midquote price series.
`midquote_lag`	a numeric vector containing the first lag of the midquote series.

The function estimates for given data the trade indicator model of Huang & Stoll (1997) using the modified estimation approach by Theissen & Zehnder (2015). The latter authors use a two-step estimation approach to overcome the negative bias in the estimation of the effective spread. The major part of this bias appears to come from the adverse selection component θ in the trade indicator model of Huang & Stoll (HS).

The authors propose a two-step estimation procedure that results in a less biased estimation of both, the effective spread and the adverse selection component, as they explicitly account for endogeneity in their model, namely a correlation between the trade innovation and the public information arrival. The estimation procedure involves a VARMA part that is estimated using the dse-package.

Standard deviations for the VARMA model (step 2) are estimated using the delta method.

A data.frame with the following values:

n: the number of observation used in estimation.
a: the first parameter estimate of the OLS model in step 1, α.
a_std: the standard deviation of the first parameter estimate of the OLS model in step 1, α.
a_t: the t-value of the first parameter estimate of the OLS model in step 1, α.
a_pval: the p-value of the first parameter estimate of the OLS model in step 1, α.
psi12: the parameter (1,2) of the VARMA model in step 2.
psi12_std: the standard deviation of the parameter (1,2) of the VARMA model in step 2.
psi12_tval: the t-value of the parameter (1,2) of the VARMA model in step 3.
psi12_pval: the p-value of the paramater (1,2) of the VARMA model in step 3.
varv: the variance of the trade innovation, ν from step 1.
varu: the variance of the public information arrival, u from step 2.
covuv: the covariance of the trade innovation, ν from step 1, and the public information arrival, u from step 2.
spread_eff_est1: the estimation of the effective spread using the traditional estimation approach.
spread_eff_est2: the estimation of the effective spread using the modified estimation approach.
spread_eff_emp: the empirical effective spread measured from the data.
spread_eff_emp_std: the standard deviation of the empirical effective spread calculated as the standard deviation of the series q_t(p_t-m_t).
spread_eff_emp_se: the standard error of the estimated empirical effective spread using the formula SE=STD/√ n.
spread_eff_emp_med: the median of the empirical effective spread calculated as the median of the series q_t(p_t-m_t).
stable: the stability of the estimated polynomials, i.e. if all roots are inside the unit circle.