Gas shocks shape euro area inflation expectations
An ECB working paper analyzes how natural gas market shocks influence euro area inflation expectations and actual inflation. It finds that while pipeline and LNG supply shocks similarly affect realized prices, LNG's impact on expectations is limited, with precautionary and industrial demand shocks being key drivers.
Granular shock identification
The paper introduces a novel Bayesian vector autoregressive (BVAR) model to identify five distinct types of shocks in the European natural gas market.
These include pipeline gas supply, global liquefied natural gas (LNG) supply, industrial demand, weather-related demand, and precautionary demand shocks.
This granular identification strategy, a key contribution, distinguishes between regional pipeline disruptions and global LNG dynamics, which is crucial given Europe's increasing reliance on LNG.
The analysis leverages high-frequency weekly data from 2018 to 2024, offering a more timely assessment of market developments than previous studies.
This allows for a deeper understanding of how these varied shocks propagate through the market and affect inflation expectations.
Precautionary demand's hidden role
The study reveals that pipeline and LNG supply shocks have comparable effects on realized gas prices and actual inflation.
However, LNG supply shocks exert a more limited influence on inflation expectations, suggesting a disconnect between observed dynamics and investor perceptions.
Precautionary demand shocks, reflecting investor sentiment about future shortages, emerge as significant short-term drivers of inflation expectations.
Industrial demand shocks also play a crucial role in the medium term, given their broader macroeconomic relevance beyond the gas market itself.
Sentiment matters for policy
This research offers a crucial new lens for central banks.
It emphasizes the global context needed for gas market assessments, where sentiment, driven by precautionary demand, can significantly influence inflation expectations.
Policymakers must leverage granular data to distinguish shock types for more effective responses.