Package 'AMIM'

AMIM roll

Description

This function computes the rolling window AMIM for a given data.table

Usage

AMIM.roll(
  data.table,
  identity.col,
  Date.col,
  rollWindow,
  return.col,
  min.obs,
  max.lag
)

Arguments

data.table	data.table with the data
identity.col	column name of the identity intrument for example the stock ticker
Date.col	column name of the date column with format "YYYY-mm-dd" (for example "2019-12-01")
rollWindow	number of days to compute the AMIM
return.col	column name of the return column
min.obs	minimum number of observations to compute the AMIM
max.lag	maximum number of lags to compute the MIM and then AMIM. The algorithm will select the number of lags that minimize the AIC but the maximum number of lags is limited by this parameter. In case the AIC is zero for the zero lag then the algorithm will estimate an AR(1) model. This is to avoid zero in the MIM and AMIM.

Value

data.table with the MIM, AMIM and the number of lags used to compute the MIM, AMIM, confidence interval (CI), and the number of lags (N).

Examples

library(AMIM)
library(data.table)
data <- AMIM::exampledata # load the example data
AMIM <- AMIM.roll(
  data.table = data, identity.col = "ticker", rollWindow = 60,
  Date.col = "Date", return.col = "RET", min.obs = 30, max.lag = 10
)

AMIM[, .SD[(.N - 5):(.N), ], by = ticker] # Last 5 rows of each instrument

---

Confidence Interval Data to compute AMIM

Description

Confidence Interval Data to compute AMIM

Usage

CI

Format

## 'CI' A data datatable with the following columns:

N

Number of lags

a

Scale parameter equal to 1 as in Tran & Leivrik (2019)

CI

Confidence interval accordingly each number lags and scale parameter

...

Source

Tran & Leivrik (2019)

---

Example Data to compute AMIM

Description

Example Data to compute AMIM

Usage

exampledata

Format

## 'exampledata' A data datatable with the following columns:

Date

Date format YYYY-MM-DD

ticker

Imaginary ticker

RET

Imaginary return

...

Source

Vu Le Tran

---