Real Options Analyzer Skill

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

# 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

# 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

# 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

# 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

{
  "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

{
  "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

Agent Skills: Real Options Analyzer

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