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arxiv: 2507.23505 · v2 · pith:MJLQNPPTnew · submitted 2025-07-31 · 📊 stat.AP

The effect of a new power interconnector on energy prices volatility: the case of Sicily

Francesco Lisi , Pierdomenico Duttilo , Marina Bertolini This is my paper

Pith reviewed 2026-05-23 23:09 UTC · model grok-4.3

classification 📊 stat.AP
keywords electricity price volatilityinterconnector impactGARCH modelSicily electricity marketintervention analysisenergy market integrationprice stability
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The pith

The Sorgente-Rizziconi interconnector increased price volatility in Sicily without lowering average prices or affecting other zones.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper studies the impact of the new power line connecting Sicily to the Italian mainland on electricity price behavior. It applies a semi-parametric GARCH model with a logistic function to capture the intervention effect on price variance starting from the 2016 commissioning date. The results indicate a rise in volatility in Sicily after the connection, with no change in mean prices and no similar effects in other regions. A non-parametric model confirms the finding. This matters because it shows that market integration through infrastructure can lead to greater price fluctuations in some contexts, affecting how future energy projects are evaluated for stability.

Core claim

The new interconnector significantly increased price volatility in Sicily, without reducing average price levels. No significant effects were observed in other Italian market zones. The analysis used daily data from 2015 to 2018 and applied a semi-parametric GARCH model with a logistic intervention function to estimate changes in conditional price variance, with a fully non-parametric additive model as robustness check.

What carries the argument

Semi-parametric GARCH model with logistic intervention function to isolate the effect of the 28 May 2016 commissioning on conditional price variance.

If this is right

  • Adding an interconnector does not necessarily reduce average electricity prices.
  • Physical integration can increase price volatility in the newly connected zone.
  • Effects of interconnectors are context-dependent and may not generalize across market zones.
  • Energy policy should account for potential volatility increases when planning infrastructure investments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar projects in other poorly connected regions could see volatility rises if local supply conditions differ from the mainland.
  • Risk management strategies for electricity traders in Sicily may need adjustment post-integration.
  • Further analysis could examine whether regulatory changes or market rules accompanied the physical link and influenced the outcome.

Load-bearing premise

That the timing and form of the intervention can be precisely captured by the logistic function so that the model attributes the volatility change specifically to the interconnector rather than other simultaneous factors.

What would settle it

Finding no volatility increase when re-estimating the model after adding controls for other events around May 2016 or when applying the same method to a control zone without an interconnector.

Figures

Figures reproduced from arXiv: 2507.23505 by Francesco Lisi, Marina Bertolini, Pierdomenico Duttilo.

Figure 1
Figure 1. Figure 1: The six Italian electricity market zones with a zoom on the Sorgente-Rizziconi [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Left: series of the mean daily electricity price in Sicily between 2015 and 2018. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Left: ACF of the original price in Sicily. Right: ACF of the model residuals. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The estimated effect of the RES production on the price according to model ( [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Estimated intervention functions for the six zones on the same scale. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Estimated conditional variance for the six zones on the same scale. The dashed [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sicily. Effects of RES on the conditional variance. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Estimated intervention functions for the six zones on the same scale. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Estimated conditional variance for the six zones on the same scale. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
read the original abstract

Integrating energy islands into the European electricity market is a key challenge for the energy transition. This study investigates the impact of the Sorgente-Rizziconi interconnector on electricity price volatility in Sicily. Before its commissioning on 28 May 2016, the Sicilian electricity market zone was poorly interconnected with the Italian mainland. Using daily data from 2015 to 2018, the analysis applies a semi-parametric GARCH model with a logistic intervention function to estimate changes in conditional price variance. A fully non-parametric additive model is employed as a robustness check, allowing the data to shape volatility dynamics without imposing a predefined structure. The results reveal that the new interconnector significantly increased price volatility in Sicily, without reducing average price levels. No significant effects were observed in other Italian market zones. These findings highlight the context-dependent nature of infrastructure impacts and suggest that physical integration alone does not guarantee price stability. The results have important implications for energy policy, investment planning, and risk management in electricity markets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper investigates the causal impact of the Sorgente-Rizziconi interconnector (commissioned 28 May 2016) on Sicilian electricity price volatility using daily 2015–2018 data. It employs a semi-parametric GARCH model that incorporates a logistic intervention function to capture the shift in conditional variance, supplemented by a fully non-parametric additive model as a robustness check. The central claim is that the interconnector raised volatility in Sicily without lowering average price levels, with no analogous effects detected in other Italian market zones.

Significance. If the identification strategy holds, the result is significant for energy economics and infrastructure policy: it demonstrates that physical market integration can raise rather than dampen price volatility in an island context, underscoring the importance of complementary measures. The dual use of a semi-parametric GARCH with an explicit intervention term and a non-parametric additive model provides a useful robustness contrast that is not common in the literature.

major comments (2)
  1. [Methodology] Methodology section (logistic intervention term in the semi-parametric GARCH): the central claim attributes post-28 May 2016 shifts in conditional variance exclusively to the interconnector via the logistic function. This identification is load-bearing yet rests on the untested assumption that no Sicily-specific time-varying confounders (renewable penetration changes, demand shocks, or regulatory adjustments) coincide with the commissioning date; the paper provides no additional controls, event-study windows, or sensitivity checks to alternative transition dates or functional forms.
  2. [Results] Results section (placebo and robustness): while the absence of effects in other Italian zones is reported, the manuscript does not present falsification tests that vary the intervention timing within Sicily or compare pre-trends in volatility drivers; without these, the Sicily-specific causal interpretation cannot be distinguished from model assumptions about the logistic shape and transition window.
minor comments (2)
  1. [Abstract] Abstract: headline quantitative estimates (magnitude of volatility change, standard errors, or p-values) are omitted; including them would allow readers to assess economic significance immediately.
  2. [Methodology] Notation: the precise functional form of the logistic intervention (location, scale, and any interaction terms with the GARCH variance equation) should be written explicitly as an equation rather than described only in prose.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each major comment below and will incorporate additional robustness checks in the revised version.

read point-by-point responses

  1. Referee: [Methodology] Methodology section (logistic intervention term in the semi-parametric GARCH): the central claim attributes post-28 May 2016 shifts in conditional variance exclusively to the interconnector via the logistic function. This identification is load-bearing yet rests on the untested assumption that no Sicily-specific time-varying confounders (renewable penetration changes, demand shocks, or regulatory adjustments) coincide with the commissioning date; the paper provides no additional controls, event-study windows, or sensitivity checks to alternative transition dates or functional forms.

    Authors: We agree that the identification assumption is central and that additional checks would strengthen the analysis. The commissioning date was determined by the physical completion of the infrastructure project, which provides some exogeneity, but we acknowledge that concurrent Sicily-specific factors cannot be ruled out without further tests. In the revision we will add: (i) sensitivity analyses replacing the logistic intervention with a simple step function and with alternative transition windows centered on May 2016; (ii) explicit controls for renewable generation shares (wind and solar) and demand in the GARCH variance equation; and (iii) event-study style plots of volatility drivers around the commissioning date. The non-parametric additive model already relaxes the logistic functional-form assumption and yields qualitatively similar results. revision: yes

  2. Referee: [Results] Results section (placebo and robustness): while the absence of effects in other Italian zones is reported, the manuscript does not present falsification tests that vary the intervention timing within Sicily or compare pre-trends in volatility drivers; without these, the Sicily-specific causal interpretation cannot be distinguished from model assumptions about the logistic shape and transition window.

    Authors: We will add the requested falsification exercises. Specifically, we will estimate the model using placebo intervention dates both before (e.g., 2015) and after (e.g., late 2016) the true commissioning date within Sicily and report whether the volatility shift appears only at the actual date. We will also compare pre-2016 trends in volatility determinants (renewable penetration, demand volatility) between Sicily and the other zones to assess differential pre-trends. These tests will be presented in a new subsection of the results and will be used to qualify the causal interpretation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical estimation on external data with standard models

full rationale

The paper applies a semi-parametric GARCH model with logistic intervention and a non-parametric additive model to daily electricity price series from 2015-2018. No equations reduce a claimed prediction to a fitted parameter by construction, no load-bearing self-citations justify uniqueness or ansatzes, and the central volatility effect is estimated from observed data rather than defined into existence. The derivation chain is self-contained against external benchmarks and does not exhibit any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are identifiable beyond the standard modeling assumptions implicit in GARCH and intervention analysis.

pith-pipeline@v0.9.0 · 5709 in / 1162 out tokens · 23218 ms · 2026-05-23T23:09:03.875432+00:00 · methodology

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