Package 'NetVAR'

Title: Network Structures in VAR Models
Description: Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.
Authors: Simon Trimborn [aut, cre]
Maintainer: Simon Trimborn <[email protected]>
License: GPL (>=3)
Version: 0.1-2
Built: 2026-06-06 13:30:22 UTC
Source: https://github.com/simontrimborn/netvar

Help Index

The LISAR model

Description

LISAR contains the LISAR model described in Zhang and Trimborn (2023). The model is computed based on 3 lambda tuning parameters which are automatically determined by the package. A scaling parameter boosts/lowers the strength of the regularization on assets which values are determined less/more important than others (influencers). The function allows to compute the LISAR model under "LASSO", "SCAD", "AdapLASSO", and "Elastic.Net".

Usage

LISAR(
  x,
  Model = "LISAR.LASSO",
  eval.criteria = "MSFE",
  Lags = 3,
  alpha.pens = 0.5,
  gamma.pens = 0.5,
  delta.elastic.net.pens = 0.5,
  lambda1_seq = 0.5,
  lambda2_seq = 0.5,
  lambda3_seq = 0.5,
  a.pen = 3.7,
  standardise = TRUE,
  eps1 = 1e-04,
  eps2 = 1e-04,
  T1 = NULL,
  T2 = NULL,
  reoptim = FALSE
)

Arguments

x

A matrix containing the data with rows being observations and columns being time series.
Model

The LISAR model to use with either LASSO "LISAR.LASSO", SCAD "LISAR.SCAD", Adaptive LASSO "LISAR.AdapLASSO", or Elastic Net "LISAR.Elastic.Net" regularization. Default "LISAR.LASSO".
eval.criteria

The evaluation criteria to use to choose the best model under the regularization parameters. Can be either "MSFE", "AIC" or "BIC". Default "MSFE".
Lags

The maximum number of lags to consider. Default 3.
alpha.pens

A number or vector specifying the boosting parameter increasing/decreasing the strength of regularization. Should be a number(s) between (0,1). See Zhang and Trimborn (2023) for details on the alpha parameters.
gamma.pens

A number or vector specifying the adaptive parameter for "LISAR.AdapLASSO". Only required for "LISAR.AdapLASSO". The number(s) should be larger than 0.
delta.elastic.net.pens

A number or vector specifying the elastic net parameter for "LISAR.Elastic.Net". Only required for "LISAR.Elastic.Net". The number(s) should be between (0,1).
lambda1_seq

The factor by which the regularization sequence of lambda1, regularizing the lag structure, decreases towards 0. Should be a value between (0,1).
lambda2_seq

The factor by which the regularization sequence of lambda2, indicating which time series are more influential (influencers), decreases towards 0. Should be a value between (0,1).
lambda3_seq

The factor by which the regularization sequence of lambda3, regularizing the individual parameters, decreases towards 0. Should be a value between (0,1).
a.pen

The parameter specifying by which the SCAD penalty taperes off towards no regularization. Only required for "LISAR.SCAD". The number should be larger than 0. Default 3.7.
standardise

Logical. If TRUE, then the data are column-wise demeaned and scaled with a GARCH(1,1) model. If set to "FALSE", it is recommended to scale the data by other means.
eps1

Control parameter for the inner optimization algorithm. The algorithm converged when between optimization steps the parameters change by less than "eps1". Default 0.0001.
eps2

Control parameter for the outer optimization algorithm. The algorithm converged when between optimization steps the parameters change by less than "eps2". Default 0.0001.
T1

A numeric stating the row of "x" where the training data end and the evaluation period starts. If NULL, then the first third of data are chosen as training data. Defaults to NULL.
T2

A numeric stating the row of "x" where the evaluation data end and the out-of-sample period starts. If NULL, then the second third of data are chosen as evaluation data. Defaults to NULL.
reoptim

Logical. If TRUE, then the best model found under the initially derived lambda sequences is further optimized by a new lambda sequence around the previous best solution. Stops when a more granular lambda sequence no longer improves the model under "eval.criteria" criterion. Default "FALSE".

Value

An object of the class netstruc with the components

Model.optimal

a list containing the optimal model, evaluation criteria and model regularizers
data.training

the training data
data.evaluation

the evaluation data
data.outofsample

the out-of-sample data.
Model.estimation

estimation specifics for the model

References

Kexin Zhang, Simon Trimborn (2023). Influential assets in Large-Scale Vector Autoregressive Models SSRN Working paper. doi:10.2139/ssrn.4619531

Examples

# Investigate influential assets relationship between 3 assets only
data(TradingData)

LISAR(TradingData[,c("AIG", "AXP", "BAC")], Model = "LISAR.LASSO", Lags = 1)

# Reoptimise the penalisation sequence
LISAR(TradingData[,c("AIG", "AXP", "BAC")], Model = "LISAR.LASSO", Lags = 1, reoptim = TRUE)


# Investigate influential assets relationship between the entire asset universe with starting values
LISAR(TradingData, Model = "LISAR.LASSO")

# Reoptimise the penalisation sequence
LISAR(TradingData, Model = "LISAR.LASSO", reoptim = TRUE)

# Consider a different model
LISAR(TradingData, Model = "LISAR.SCAD", Lags = 1)

Plotting function for objects of class NetVAR

Description

Plots an object of class NetVAR

Usage

## S3 method for class 'NetVAR'
plot(x, join_graphs = TRUE, scale_range = TRUE, legend = FALSE, ...)

Arguments

x

An object of class NetVAR
join_graphs

Logical. If TRUE, then heatmaps for each lagged parameter matrix are returned in one plot. If FALSE, a single figure per heatmap is generated.
scale_range

Logical. If TRUE, parameters larger 1 or smaller -1 are truncated to 1 and -1 purely for visualisation purposes. If FALSE, then the actual values are plotted in the heatmaps.
legend

Add a legend to plots. Default FALSE
...

Further arguments, currently none.

Value

None

Printing function for objects of class NetVAR

Description

print.NetVAR prints the values of an object of class NetVAR.

Usage

## S3 method for class 'NetVAR'
print(x, ...)

Arguments

x

An object of class NetVAR
...

Further arguments to be passed over. Currently none.

Value

None

Printing function for objects of class NetVAR

Description

summary.NetVAR prints the values of an object of class NetVAR.

Usage

## S3 method for class 'NetVAR'
summary(object, ...)

Arguments

object

An object of class NetVAR
...

Further arguments to be passed over. Currently none.

Value

None

Trading data for 11 US stocks from the finance sector

Description

A dataset containing the 5 minute log returns of 11 US stocks for the 22.02.2022.

Format

A matrix with 5 minute trading data for 11 stocks on 22.02.2022.

Source

Yahoo-Finance.