Agent skill

real-options-analyzer

Real options valuation skill for analyzing strategic flexibility and investment timing decisions

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/real-options-analyzer

Metadata

Additional technical details for this skill

domain
business
category
risk
priority
lower
specialization
decision-intelligence
tools libraries 
[
    "numpy",
    "scipy",
    "custom implementations"
]

SKILL.md

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

  1. Identify all relevant options before valuation
  2. Estimate volatility from comparable assets or market data
  3. Use American option models for flexible exercise timing
  4. Consider interaction between multiple options
  5. Validate inputs with sensitivity analysis
  6. Communicate option value as "value of flexibility"
  7. 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

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