Package 'GARCHIto'

Title: Class of GARCH-Ito Models
Description: Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.
Authors: Xinyu Song
Maintainer: Xinyu Song <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2024-10-26 06:29:43 UTC
Source: CRAN

Help Index

Realized GARCH-Ito Model

Description

Estimate model parameters for the Realized GARCH-Ito Model

Usage

RealizedEst(RV = RV, JV = NULL)

Arguments

RV

Time series of daily realized volatilities.
JV

Time series of daily jump variations,

Value

Estimated parameter values and daily conditional volatilities:

coefficients

parameter estimates of the realized GARCH-Ito model

sigma

daily conditional volatility estimates of the realized GARCH-Ito model

pred

one-step-ahead predicted volatility value

References

Song, X., Kim, D., Yuan, H., Cui, X., Lu, Z., Zhou, Y., & Wang, Y. (2020). Volatility Analysis with Realized GARCH-Ito Models. Journal of Econometrics, in press.

Examples

sample_data
RealizedEst(sample_data$RV)
RealizedEst(sample_data$BPV, sample_data$JV)

Realized GARCH-Ito Model with Options

Description

Estimate model parameters for the Realized GARCH-Ito Model with Options

Usage

RealizedEst_Option(RV = RV, JV = NULL, NV = NULL, homogeneous = TRUE)

Arguments

RV

Time series of daily realized volatilities.
JV

Time series of daily jump variations,
NV

Time series of daily volatilities estimated using option data
homogeneous

Whether to assume homogeneous error in the linear regression model between conditional volatility of the realized GARCH-Ito model and volatility estimated from the option data, default is TRUE.

Value

Estimated parameter values and daily conditional volatilities:

coefficients

parameter estimates of the realized GARCH-Ito model

sigma

daily conditional volatility estimates of the realized GARCH-Ito model

pred

one-step-ahead predicted volatility value

References

Song, X., Kim, D., Yuan, H., Cui, X., Lu, Z., Zhou, Y., & Wang, Y. (2020). Volatility Analysis with Realized GARCH-Ito Models. Journal of Econometrics, in press.

CSI 300 Index Realized Measures

Description

This sample data set contains realized measures, such as realized volatility (RV), bi-power realized volatility (BPV) and jump variation (JV) estimated from CSI 300 Index high-frequency data, it also includes daily low-frequency log returns (return).

Usage

sample_data

Format

An object with the following elements:

RV

times series of daily realized volatility estimates

BPV

times series of daily bi-power realized volatility estimates

JV

time series of daily jump variation estimates

return

time series of daily low-frequency returns

Unified GARCH-Ito Models

Description

Estimate model parameters for the Unified GARCH-Ito Model.

Usage

UnifiedEst(RV = RV, return = return)

Arguments

RV

Time series of daily realized volatilities.
return

Time series of daily log returns.

Value

Estimated parameter values and daily conditional volatilities:

coefficients

parameter estimates of the realized GARCH-Ito model

sigma

daily conditional volatility estimates of the realized GARCH-Ito model

pred

one-step-ahead predicted volatility value

References

Kim, D. & Wang, Y. (2016). Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data. Journal of Econometrics. 194:220-230.

Examples

sample_data
UnifiedEst(sample_data$RV, sample_data$return)