compute_mlpin: Computes the probability of informed trading from ML...

In simonsays1980/bayespin: An R package for Bayesian estimation of the probability of informed trading

Description

Calling compute_mlpin() returns the probability of informed trading as presented in the paper of Easley et al. (1996).

The estimation procedure in estimate_mlekop() uses the logits of the rates alpha and delta to ensure a more stable optimization. To arrive at the original parameter estimates the logistic transformation from logistic() is applied.

Usage

compute_mlpin(par)

Arguments

par	A vector containing the parameter estimates from the optimization procedure.

Value

A double holding the PIN estimate.

References

• Easley, D., Kiefer, N., O’Hara, M., Paperman, J., 1996. Liquidity, information, and infrequently traded stocks. Journal of Finance 51, 1405–1436.

See Also

• estimate_mlekop() for the calling function.

Examples

# Simulate trades data.
trades_data <- simulate_ekop()
# Estimate the EKOP model.
pin_optml <- estimate_mlekop(trades_data, methodLik="approx", 
                             fnLik="compute_ekop_orig_lik")
# Estimate the PIN from the parameter estimates.
compute_mlpin(pin_optml$par)

simonsays1980/bayespin documentation built on Dec. 23, 2021, 2:25 a.m.