ets: ets Bayesian Network

In bnRep: A Repository of Bayesian Networks from the Academic Literature

ets Bayesian Network

Description

Uncovering drivers of EU carbon futures with Bayesian networks.

Format

A discrete Bayesian network to model the influence of financial, economic, and energy-related factors on EUA futures prices. The model was given in the referenced paper. The vertices are:

CAC

(High, Low, Neutral);

CO1

(High, Low, Neutral);

DAX

(High, Low, Neutral);

ECO

(High, Low, Neutral);

EURCHF

(High, Low, Neutral);

EURCNY

(High, Low, Neutral);

EURGBP

(High, Low, Neutral);

EURRUB

(High, Low, Neutral);

EURUSD

(High, Low, Neutral);

LBEATREU

(High, Low, Neutral);

LB01TREU

(High, Low, Neutral);

MO1

(High, Low, Neutral);

MXEU0EN

(High, Low, Neutral);

NG1COMB

(High, Low, Neutral);

SPGTCED

(High, Low, Neutral);

SPX

(High, Low, Neutral);

SXXP

(High, Low, Neutral);

VIX

(High, Low, Neutral);

XA1

(High, Low, Neutral);

XAU

(High, Low, Neutral);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

Maciejowski, J., & Leonelli, M. (2025). Uncovering Drivers of EU Carbon Futures with Bayesian Networks. arXiv preprint arXiv:2505.10384.

bnRep documentation built on June 8, 2025, 9:34 p.m.