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Term: Black Scholes

Term: Black Scholes

“The Black-Scholes model (or Black-Scholes-Merton model) is a fundamental mathematical formula that calculates the theoretical fair price of European-style options, using inputs like the underlying stock price, strike price, time to expiration, risk-free interest rate and volatility.” – Black Scholes

Black-Scholes Model (Black-Scholes-Merton Model)

The Black-Scholes model, also known as the Black-Scholes-Merton model, is a pioneering mathematical framework for pricing European-style options, which can only be exercised at expiration. It derives a theoretical fair value for call and put options by solving a parabolic partial differential equation—the Black-Scholes equation—under risk-neutral valuation, replacing the asset’s expected return with the risk-free rate to eliminate arbitrage opportunities.1,2,5

Core Formula and Inputs

The model prices a European call option ( C ) as:

C = S_0 N(d_1) - K e^{-rT} N(d_2)

where:

  • ( S_0 ): current price of the underlying asset (e.g., stock).3,7
  • ( K ): strike price.5,7
  • ( T ): time to expiration (in years).5,7
  • ( r ): risk-free interest rate (constant).3,7
  • (\sigma ): volatility of the underlying asset’s returns (annualised).2,7
  • ( N(\cdot) ): cumulative distribution function of the standard normal distribution.
  • d_1 = \frac{\ln(S_0 / K) + (r + \sigma^2 / 2)T}{\sigma \sqrt{T}}
  • d_2 = d_1 - \sigma \sqrt{T}1,2,5

A symmetric formula exists for put options. The model assumes log-normal distribution of stock prices, meaning continuously compounded returns are normally distributed:

\ln S_T \sim N\left( \ln S_0 + \left( \mu - \frac{\sigma^2}{2} \right)T, \sigma^2 T \right)

where ( \mu ) is the expected return (replaced by ( r ) in risk-neutral pricing).2

Key Assumptions

The model rests on idealised conditions for mathematical tractability:

  • Efficient markets with no arbitrage and continuous trading.1,3
  • Log-normal asset returns (prices cannot go negative).2,3
  • Constant risk-free rate ( r ) and volatility ( \sigma ).3
  • No dividends (original version; later adjusted by replacing ( S_0 ) with ( S_0 e^{-qT} ) for continuous dividend yield ( q ), or subtracting present value of discrete dividends).2,3
  • No transaction costs, taxes, or short-selling restrictions; frictionless trading with a risky asset (stock) and riskless asset (bond).1,3
  • European exercise only (no early exercise).1,5

These enable delta hedging: dynamically adjusting a portfolio of the underlying asset and riskless bond to replicate the option’s payoff, making its price unique.1

Extensions and Limitations

  • Dividends: Adjust ( S_0 ) to ( S_0 - PV(\text{dividends}) ) or use yield ( q ).2
  • American options: Use Black’s approximation, taking the maximum of European prices with/without dividends.2
  • Greeks: Measures sensitivities like delta (\Delta = N(d_1)), vega (volatility sensitivity), etc., for risk management.4
    Limitations include real-world violations (e.g., volatility smiles, jumps, stochastic rates), but it remains foundational for derivatives trading, valuation (e.g., 409A for startups), and extensions like binomial models.3,5,7

Best Related Strategy Theorist: Myron Scholes

Myron Scholes (b. 1941) is the most directly linked theorist, co-creator of the model and Nobel laureate whose work revolutionised options trading and risk management strategies.

Biography

Born in Timmins, Ontario, Canada, Scholes earned a BA (1962), MA (1964), and PhD (1969) in finance from the University of Chicago, studying under Nobel winners like Merton Miller. He taught at MIT (1968–1972, collaborating with Fischer Black and Robert Merton), Stanford (1973–1996), and later Oxford. In 1990, he co-founded Long-Term Capital Management (LTCM), a hedge fund using advanced models (including Black-Scholes variants) for fixed-income arbitrage, which amassed $4.7 billion in assets before collapsing in 1998 due to leverage and Russian debt crisis—prompting a $3.6 billion Federal Reserve bailout. Scholes received the 1997 Nobel Prize in Economics (shared with Merton; Black deceased), cementing his legacy. He now advises at Platinum Grove Asset Management and philanthropically supports education.1

Relationship to the Term

Scholes co-authored the seminal 1973 paper “The Pricing of Options and Corporate Liabilities” with Fischer Black (1938–1995), an economist at Arthur D. Little and later Goldman Sachs, who conceived the core hedging insight but died before the Nobel. Robert C. Merton (b. 1944, Merton’s 1973 paper extended it to dividends and American options) formalised continuous-time aspects, earning co-credit. Their breakthrough—published amid nascent options markets (CBOE opened 1973)—enabled risk-neutral pricing and dynamic hedging, transforming derivatives from speculative to hedgeable instruments. Scholes’ strategic insight: options prices reflect volatility alone under no-arbitrage, powering strategies like volatility trading, portfolio insurance, and structured products at banks/hedge funds. LTCM exemplified (and exposed limits of) scaling these via leverage.1,2,5

 

References

1. https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model

2. https://analystprep.com/study-notes/frm/part-1/valuation-and-risk-management/the-black-scholes-merton-model/

3. https://carta.com/learn/startups/equity-management/black-scholes-model/

4. https://www.columbia.edu/~mh2078/FoundationsFE/BlackScholes.pdf

5. https://www.sofi.com/learn/content/what-is-the-black-scholes-model/

6. https://gregorygundersen.com/blog/2024/09/28/black-scholes/

7. https://corporatefinanceinstitute.com/resources/derivatives/black-scholes-merton-model/

8. https://www.youtube.com/watch?v=EEM2YBzH-2U

9. https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/black-scholes/v/introduction-to-the-black-scholes-formula

 

"The Black-Scholes model (or Black-Scholes-Merton model) is a fundamental mathematical formula that calculates the theoretical fair price of European-style options, using inputs like the underlying stock price, strike price, time to expiration, risk-free interest rate and volatility." - Term: Black Scholes

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Term: Price Elasticity

Term: Price Elasticity

Price elasticity measures how sensitive customer demand is to changes in price. By understanding whether demand for a product is elastic (highly responsive to price changes) or inelastic (less responsive), businesses can optimize pricing to maximize revenue, profit and market share. Effective use of price elasticity enables data-driven pricing decisions, supports dynamic and value-based pricing models, and helps forecast the impact of price adjustments on sales and profitability.

Comprehensive Outline of Pricing Elasticity in Pricing Strategy

1. Definition and Core Concept

  • Price elasticity of demand quantifies the responsiveness of quantity demanded to a change in price.

  • Expressed as:

    Price Elasticity of Demand=% Change in Quantity Demanded% Change in Price

  • Elastic demand: Large change in quantity for a small price change.

  • Inelastic demand: Little change in quantity for a price change.

2. Importance in Pricing Strategy

  • Guides businesses on how much they can raise or lower prices without significantly affecting demand.

  • Helps forecast revenue and profit impacts of pricing decisions.

  • Enables segmentation and tailored pricing for different products or customer groups.

3. Factors Influencing Price Elasticity

  • Availability of Substitutes: More substitutes increase elasticity.

  • Necessity vs. Luxury: Essentials tend to be inelastic; luxuries are more elastic.

  • Proportion of Income: Expensive items relative to income are more elastic.

  • Time Horizon: Elasticity increases over time as consumers adjust.

  • Brand Loyalty and Differentiation: Strong brands can reduce elasticity.

4. Pricing Strategies Based on Elasticity

Strategy When to Use Elasticity Context
Penetration Pricing To gain market share quickly High elasticity
Skimming Pricing To maximize early profits Low elasticity
Dynamic Pricing To respond to real-time demand High elasticity
Value-Based Pricing To reflect perceived value Low elasticity
Cost-Plus Pricing To cover costs with a markup Often inelastic markets
Competitive Pricing To match or beat competitors High elasticity
 

5. Practical Applications

  • Dynamic Pricing: Companies like Uber use elasticity to adjust prices in real time, balancing supply and demand.

  • Revenue Optimization: Lowering prices in elastic markets can boost sales volume and revenue; raising prices in inelastic markets can increase margins.

  • Product Segmentation: Essential goods (e.g., food, fuel) are priced with less sensitivity to demand drops, while luxury goods require careful price setting due to high elasticity.

6. Measurement and Data Requirements

  • Requires historical sales and pricing data for accurate calculation.

  • Quantitative methods: Statistical analysis, A/B testing, econometric modeling.

  • Qualitative insights: Customer surveys, market research.

7. Strategic Implications

  • Informs optimal price points for new and existing products.

  • Supports competitive positioning and differentiation.

  • Enables businesses to anticipate and react to market changes, competitor moves, and shifts in consumer preferences.

Summary:
Price elasticity is foundational to effective pricing strategy. By quantifying how demand responds to price changes, companies can make informed, data-driven decisions to optimize revenue, profit, and market position. Understanding elasticity enables the use of advanced pricing models, supports market segmentation, and helps businesses adapt to competitive and economic dynamics.

 

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PODCAST: Effective Transfer Pricing

PODCAST: Effective Transfer Pricing

Our Spotify podcast discusses how to get transfer pricing right.

We discuss effective transfer pricing within organizations, highlighting the prevalent challenges and proposing solutions. The core issue is that poorly implemented internal pricing leads to suboptimal economic decisions, resource allocation problems, and interdepartmental conflict. The hosts advocate for market-based pricing over cost recovery, emphasizing the importance of clear price signals for efficient resource allocation and accurate decision-making. They stress the need for service level agreements, fair cost allocation, and a comprehensive process to manage the political and emotional aspects of internal pricing, ultimately aiming for improved organizational performance and profitability. The podcast includes case studies illustrating successful implementations and the authors’ expertise in this field.

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PODCAST: A strategic take on cost-volume-profit analysis

PODCAST: A strategic take on cost-volume-profit analysis

Our Spotify podcast highlights that despite familiarity, most managers do not apply CVP analysis and get it wrong in its most basic form.

The hosts explain cost-volume-profit (CVP) analysis, a crucial business tool often misapplied. It details the theoretical underpinnings of CVP, using graphs to illustrate relationships between price, volume, and profit. The hosts highlight common errors in CVP application, such as neglecting volume changes after price increases, leading to the “margin-price-volume death spiral.” The hosts offer practical advice and strategic questions to improve CVP analysis and decision-making, emphasizing the need for accurate costing and a nuanced understanding of market dynamics. Finally, the podcast provides case studies illustrating both successful and unsuccessful CVP implementations.

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Strategy Tools: ‘Price-Volume-Profit’ Part 1 – A strategic take on cost-volume-profit analysis

Strategy Tools: ‘Price-Volume-Profit’ Part 1 – A strategic take on cost-volume-profit analysis

By Eric van Heeswijk and Marc Wilson
Eric is an analyst and Marc is a partner at Global Advisors. Both are based in Johannesburg, South Africa.

Almost every person who has studied financial or management accounting at school or university is probably familiar with cost-volume-profit (CVP) analysis. It should be the basis of financial planning in most companies. However, in our experience, most managers do not apply the analysis and get it wrong in its most basic form (e.g. planning for similar / increased volumes together with price increases). The outcome? At best: results that fail to meet budgets. At worst: firms trigger the “margin-price-volume death spiral”. Whether you are a production manager or a CEO, you should understand how CVP analysis applies to your firm. Your business’s survival may be at stake.

Read more at:
https://globaladvisors.biz/blog/2019/11/28/strategy-tools-price-volume-profit-part-1-a-strategic-take-on-cost-volume-profit-analysis/

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Global Advisors | Quantified Strategy Consulting