Real Options Analyzer

Overview

The Real Options Analyzer skill provides capabilities for valuing strategic flexibility in investment decisions. It extends traditional NPV analysis by quantifying the value of options to defer, expand, contract, abandon, or switch, enabling better decision-making under uncertainty.

Capabilities

•Option identification and framing
•Binomial tree valuation
•Black-Scholes adaptation
•Monte Carlo option valuation
•Decision tree representation
•Sensitivity to volatility
•Strategic option types (defer, expand, abandon, switch)
•Integration with NPV analysis

Used By Processes

•Strategic Scenario Development
•What-If Analysis Framework
•Investment Decision Analysis

Usage

Option Definition

python

# Define real option
real_option = {
    "type": "option_to_expand",
    "underlying_project": {
        "name": "Manufacturing Plant Phase 1",
        "base_npv": 5000000,
        "initial_investment": 20000000,
        "volatility": 0.35,  # annual volatility of project value
        "dividend_yield": 0.03  # cash flow yield
    },
    "option_characteristics": {
        "expansion_cost": 15000000,
        "expansion_factor": 1.5,  # 50% capacity increase
        "exercise_window": {"start_year": 2, "end_year": 5},
        "option_type": "American"  # can exercise anytime in window
    },
    "risk_free_rate": 0.05
}

Binomial Tree Valuation

python

# Binomial tree configuration
binomial_config = {
    "method": "binomial_tree",
    "parameters": {
        "steps": 50,
        "up_factor": "calculated",  # u = exp(sigma * sqrt(dt))
        "down_factor": "calculated",  # d = 1/u
        "risk_neutral_probability": "calculated"
    },
    "outputs": {
        "option_value": True,
        "optimal_exercise_boundary": True,
        "tree_visualization": True
    }
}

Black-Scholes Adaptation

python

# Black-Scholes configuration
bs_config = {
    "method": "black_scholes",
    "parameters": {
        "current_value": 25000000,  # S: current project value
        "exercise_price": 15000000,  # K: investment to exercise
        "time_to_expiry": 3,  # T: years
        "volatility": 0.35,  # sigma
        "risk_free_rate": 0.05,  # r
        "dividend_yield": 0.03  # q: continuous cash flow yield
    },
    "option_type": "call"  # expansion = call, abandonment = put
}

Monte Carlo Valuation

python

# Monte Carlo for path-dependent options
monte_carlo_config = {
    "method": "monte_carlo",
    "simulations": 50000,
    "path_model": {
        "type": "geometric_brownian_motion",
        "parameters": {
            "drift": 0.08,
            "volatility": 0.35
        }
    },
    "exercise_strategy": "least_squares_monte_carlo",  # LSM for American options
    "basis_functions": ["laguerre", 3]  # polynomial basis
}

Real Option Types

Option Type	Description	Analogy
Defer	Wait for better information	Call option
Expand	Increase scale if successful	Call option
Contract	Reduce scale if unfavorable	Put option
Abandon	Exit and recover salvage	Put option
Switch	Change inputs/outputs	Portfolio of options
Compound	Option on an option	Sequential investment
Rainbow	Multiple sources of uncertainty	Multi-asset option

Input Schema

json

{
  "option_type": "defer|expand|contract|abandon|switch|compound",
  "underlying_project": {
    "current_value": "number",
    "volatility": "number",
    "dividend_yield": "number"
  },
  "option_terms": {
    "exercise_price": "number",
    "time_to_expiry": "number",
    "exercise_type": "European|American"
  },
  "valuation_method": "binomial|black_scholes|monte_carlo",
  "parameters": "object",
  "sensitivity_analysis": {
    "variables": ["volatility", "time", "value"],
    "ranges": "object"
  }
}

Output Schema

json

{
  "option_value": "number",
  "expanded_npv": "number",
  "static_npv": "number",
  "flexibility_value": "number",
  "greeks": {
    "delta": "number",
    "gamma": "number",
    "vega": "number",
    "theta": "number",
    "rho": "number"
  },
  "exercise_boundary": {
    "time": ["number"],
    "critical_value": ["number"]
  },
  "sensitivity": {
    "variable": {
      "values": ["number"],
      "option_values": ["number"]
    }
  },
  "decision_rule": "string",
  "visualization_paths": ["string"]
}

Best Practices

•Identify all relevant options before valuation
•Estimate volatility from comparable assets or market data
•Use American option models for flexible exercise timing
•Consider interaction between multiple options
•Validate inputs with sensitivity analysis
•Communicate option value as "value of flexibility"
•Compare expanded NPV to traditional NPV for decision support

Expanded NPV Framework

Expanded NPV = Static NPV + Option Value

Decision Rule:

•If Expanded NPV > 0: Proceed (even if Static NPV < 0)
•If Expanded NPV < 0 but Option Value > 0: Consider deferral
•Option Value quantifies the benefit of waiting/flexibility

Integration Points

•Feeds into Strategic Options Analyst agent
•Connects with Monte Carlo Engine for simulation
•Supports Scenario Planner for strategy valuation
•Integrates with Decision Tree Builder for representation