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Pricing Derivative Instruments in the Iranian Capital Market: A Review of Concepts and Challenges
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Working paper — under peer review Derivatives · September 2026 · 9 min read

Pricing Derivative Instruments in the Iranian Capital Market: A Review of Concepts and Challenges

Derivatives are risk-management tools, yet pricing them in the Iranian market meets structural challenges. A review of definitions, models and the theory–market gap.

Written by Reyhaneh Ashrafi linkedin.com/in/reyhaneh-ashrafi

Why derivatives matter

A derivative is a contract whose value is derived from an underlying asset: a share, a currency, a commodity or an interest rate. In its simplest definition, a derivative is a device for transferring risk: whoever does not want it sells it, whoever is ready to bear it buys it — and the price of that exchange is the subject of pricing.

In developed markets, derivatives are the backbone of risk management: a farmer locks the crop price with futures, an importer hedges the exchange rate, and a fund caps its losses with options. Without them, risks are neither removed nor priced — they simply pile up in silence.

Pricing challenges in the Iranian market

Standard pricing models — from cost-of-carry for futures to Black–Scholes for options — rest on assumptions: deep liquidity, short-selling, an observable risk-free rate and stable expectations. In Iran's capital market, each assumption carries caveats; from short-sale constraints to inflation swings that put the very notion of a risk-free rate into question.

The practical result of this structural gap is the distance between theoretical and traded prices — a distance that at times is too large to be read as an arbitrage opportunity and instead signals inefficiency and hidden risk. Measuring that distance precisely is the starting point of any serious research on Iranian derivatives.

What the review does

In this joint research we organise the Iranian literature: extracting, comparing and harmonising definitions from academic and professional sources to build a common language. Challenges are then classified in three layers: conceptual (ambiguous definitions), data (availability and quality) and structural (market constraints).

The core message is simple: before modelling advances, language and data must be fixed. A paper with unclear definitions produces incomparable results; without reliable data, any model — however sophisticated — merely makes complexity look elegant.

Key takeaways

  • A derivative transfers risk; pricing it means pricing that transfer.
  • Standard model assumptions do not fully hold in Iran, creating the gap between theory and traded prices.
  • The first research step is harmonising definitions and data, not complicating the model.

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