Embedded Files
India introduced electricity futures with a clear and sensible objective: to allow market participants—especially DISCOMs, generators, and large consumers—to hedge short-term price risk arising from volatility in the Day-Ahead Market (DAM). In theory, electricity futures should mirror expectations about future spot prices, just as they do in mature power markets globally.
Several months into their introduction, however, Indian electricity derivatives remain thinly traded, weakly correlated with physical markets, and largely irrelevant for risk management. This article analyses using NSE’s own settlement formula and actual market data
1. How NSE Electricity Futures Are Settled (No Assumptions, No Interpretation)
As per the official contract specifications, the final settlement price of electricity futures traded on NSE is defined as the simple arithmetic average of daily DAM prices during the contract month. In principle, this creates a direct mechanical link between the spot market and futures settlement. If DAM prices rise persistently, futures prices should reflect that expectation; if DAM prices soften, futures should adjust downward.
In practice, this link is weakened by market realities. DAM trading on PXIL—the reference exchange for settlement—has remained negligible in volume. During the period analysed, daily PXIL DAM volumes ranged from fractions of a megawatt-hour to a few hundred megawatt-hours, far too small to represent a meaningful national price signal. Consequently, and unavoidably, the analysis relies on DAM data from IEX, where the overwhelming majority of India’s DAM volumes are concentrated. Given India’s single synchronous grid and the non-storability of electricity, this substitution does not distort the economic interpretation.
As per the official contract specification of National Stock Exchange of India (NSE), the Final Settlement Price (FSP) of electricity futures is defined as:
The simple arithmetic average of the daily Market Clearing Price (MCP) of the Day-Ahead Market (DAM) on IEX during the contract month.
“DDR based on Average of the DAM-UMCPs (Unconstrained Market Clearing Price) * of PXIL (Power Exchange of India Ltd) of all the calendar days of the expiry month.”
2. Data Used
The analysis covers the period from August to 26 December 2025. For each month:
DAM prices are computed as simple averages of daily DAM MCPs, consistent with the settlement logic used for futures.
Futures prices are taken as monthly average settlement prices from NSE data.
All calculations use standard sample statistics (STDEV.S, COVARIANCE.S, and CORREL in Excel) to ensure internal consistency.
The objective is not to test arbitrage efficiency or intraday price formation, but to examine whether futures prices meaningfully co-move with spot prices over the same delivery horizon.
DAM MCP: Daily unconstrained MCP from IEX, aggregated month-wise using simple averages (as per NSE rules)
Futures MCP: Monthly average settlement price of NSE electricity futures
Period analysed: August to 26 December 2025
Excel files in zip are place below this.
Why Not used MCX futures data?
Because it is not available on its website –
(https://www.mcxindia.com/market-data/historical-data#)
3. Month-wise DAM vs Futures Prices (NSE Basis)
Month 2025 DAM MCP (Simple Avg, ₹/MWh) NSE Futures MCP (₹/MWh)
August 3845.79 4,195.95
September 3579.59 3,916.90
October 2669.01 3,621.39
November 3069.18 3,421.23
December 3904.84 3,459.60
4. Correlation Result: The Central Finding
Assume X is Average of IEX DAM MCP & Y is Average of NSE Futures MCP
X=3413.68 & Y=3723.01 (using STDDEV.S in Excel)
Using the above data from the table, standard deviation of DAM MCP and Future Prices are 531.2 and 328.65 and co-variance (DAM MCP, Future Prices) = 71697.62 using COVARIANCE.S in Excel
Pearson Correlation co-efficient between DAM MCP and Futures MCP is
= Covariance / (Std. Dev. DAM * Std. Dev. Futures) (Using CORREL in Excel)
= 71697.62/ (531.2*328.65)
= 0.41
5. What a 0.41 Correlation Really Tells Us
India introduced electricity futures with a clear and economically sound objective: to enable market participants—particularly DISCOMs, generators, and large consumers—to hedge short-term price risk arising from volatility in the Day-Ahead Market (DAM). In principle, electricity futures should embody expectations of future spot prices, as they do in mature power markets worldwide. A reasonably strong linkage between spot and futures prices is therefore not merely desirable but fundamental if derivatives are to serve any meaningful role in price discovery and risk management.
The Pearson correlation coefficient between monthly DAM MCP and monthly electricity futures prices is approximately 0.41, pointing to a positive but only moderate relationship between spot and forward prices.
A correlation of 0.41 indicates a positive but moderate linear relationship between spot and futures prices. The positive sign confirms that futures prices do respond to DAM outcomes and that the spot market continues to serve as a reference point for price formation. However, the magnitude of the correlation is equally important. Squaring the coefficient shows that only about 17% of the variation in futures prices can be explained by movements in DAM prices. The remaining 83% is driven by other influences.
This immediately rules out the idea that electricity futures in India are forward projections of DAM prices. Futures prices are not simply averaging expected spot prices over the delivery month. Instead, they embed a much wider set of considerations—risk, uncertainty, policy expectations, and market sentiment—that dilute the direct price-to-price linkage.
Crucially, this outcome should not be interpreted as a market failure. A moderate correlation is exactly what one would expect in an electricity system characterised by non-storability, regulatory intervention, and limited arbitrage.
6. Structural Reasons for the Moderate Linkage
Several structural features of the electricity market explain why the DAM–futures correlation is limited.
First, electricity cannot be economically stored. In commodities such as oil, gas, or coal, storage arbitrage enforces a tight relationship between spot and futures prices. Deviations invite immediate arbitrage, pulling prices back into alignment. Electricity lacks this mechanism entirely. As a result, even persistent spot price movements do not compel futures prices to converge tightly.
Second, DAM prices in India are not pure scarcity prices. They are influenced by bid caps, must-run renewable generation, transmission constraints, and post-clearing interventions by the system operator. Consequently, DAM MCP represents a hybrid administrative–economic outcome. Futures market participants recognise this and treat DAM prices as informative but noisy signals, discounting their use as clean forward benchmarks.
Third, futures prices embed substantial risk premia. These include uncertainty around fuel availability, imported fuel exposure, weather-driven demand and supply variability, regulatory interventions, deviation settlement outcomes, and emergency procurement risks. Many of these drivers are only weakly correlated with average DAM prices, yet they materially influence forward pricing decisions.
7. What the Correlation Reveals About Market Participants
The correlation of 0.41 also reflects the relative roles of hedgers, speculators, and arbitrageurs in the Indian power derivatives market.
Hedgers, particularly DISCOMs and generators, are the most economically significant participants. However, their use of futures is constrained by the absence of explicit regulatory recognition of hedging costs as pass-through items under ARR. As a result, futures are used defensively and episodically—primarily during periods of heightened uncertainty—rather than systematically to lock in expected prices. Hedging demand is therefore driven more by fear of adverse outcomes than by DAM price levels, injecting a risk premium that weakens spot–forward correlation.
Speculators play a limited role. Where they participate, behaviour tends to be volatility-driven rather than expectation-driven. Speculators respond to regime shifts, stress events, and uncertainty rather than continuously arbitraging differences between spot and futures prices. This adds responsiveness to risk but does not enforce convergence.
Arbitrageurs, who would normally discipline the relationship between spot and futures prices, are structurally constrained. Non-storability, contract design differences, and institutional frictions prevent effective inter-temporal arbitrage. Their near absence allows futures prices to deviate persistently from DAM prices without correction.
8. Temporal Mismatch and Policy Uncertainty
The moderate correlation also reflects a temporal mismatch between the two markets. DAM prices capture immediate operational conditions—weather, outages, short-term demand fluctuations—whereas futures prices represent expectations over a delivery window spanning weeks or months. In an environment characterised by policy uncertainty and limited forecasting confidence, translating short-term spot outcomes into reliable medium-term expectations is inherently difficult. Futures therefore respond only partially to DAM price movements.
9. Policy and Market Design Implications
From a policy perspective, the correlation of 0.41 sends a clear message. It shows that electricity futures in India are influenced by spot prices but are not governed by them. Futures currently function more as instruments for managing uncertainty than as mechanisms for forward price discovery. Strengthening derivatives markets alone will not materially improve price discovery unless accompanied by deeper, more credible, and less administratively distorted spot market price signals.
Equally important, the result highlights a regulatory inconsistency. DISCOMs are expected to manage price risk prudently, yet explicit recognition of financial hedging costs remains absent. This pushes risk management toward inefficient physical contracting and limits the role of formal derivatives.
10. Conclusion: What the 0.41 Really Diagnoses
The Pearson correlation coefficient of around 0.41 should not be read as evidence of disconnection between spot and futures markets. Rather, it is a diagnostic indicator of an evolving market structure. It reflects partial spot anchoring, dominant risk premia, weak arbitrage discipline, regulatory uncertainty, and constrained participant behaviour.
In short, India’s electricity futures market is not pricing expected power—it is pricing uncertainty. Until spot prices become cleaner scarcity signals and hedging is institutionally supported, futures will continue to reflect risk more than expectation.
Data Files:
https://drive.google.com/file/d/16vI_XbYsbbDcVWH3qWcICocDRPHPrGWO/view?usp=sharing
Page updated
Google Sites
Report abuse