Surface-Lib

A high-performance Rust library for volatility surface modeling and calibration. Currently supports the SVI (Stochastic Volatility Inspired) model with advanced optimization capabilities for quantitative finance applications.

Features

• SVI Model: Complete implementation of the SVI volatility model with parameter validation and no-arbitrage constraints
• Advanced Calibration: CMA-ES and L-BFGS-B optimization with robust parameter estimation
• Model Parameters: Configurable weighting schemes (ATM boost, vega weighting) for fine-tuning calibration
• Option Pricing: Black-Scholes pricing with calibrated volatility surfaces
• Production Ready: Optimized for real-time trading and backtesting systems
• Type Safety: Comprehensive error handling and parameter validation

Usage

Basic Example

use surface_lib::{
    calibrate_svi, price_with_svi, default_configs, CalibrationParams, 
    MarketDataRow, FixedParameters, SVIParams
};

// Create market data with required fields
let market_data = vec![
    MarketDataRow {
        option_type: "call".to_string(),
        strike_price: 100.0,
        underlying_price: 95.0,
        years_to_exp: 0.25,
        market_iv: 0.20,  // 20% volatility as decimal
        vega: 0.15,
        expiration: 1640995200, // Unix timestamp
    },
    // ... more data points
];

// Step 1: Calibrate SVI parameters
let config = default_configs::fast();
let calib_params = CalibrationParams::default();
let (objective, params, used_bounds) = calibrate_svi(
    market_data.clone(), 
    config, 
    calib_params,
    None, // Optional initial guess
)?;

// Step 2: Create SVI parameters from calibration results
let svi_params = SVIParams {
    t: 0.25,        // Time to expiration (should match your data)
    a: params[0],   // Base variance level
    b: params[1],   // Slope factor  
    rho: params[2], // Asymmetry parameter
    m: params[3],   // Horizontal shift
    sigma: params[4], // Curvature parameter
};

// Step 3: Price options with calibrated model
let fixed_params = FixedParameters {
    r: 0.02,  // Risk-free rate
    q: 0.0,   // Dividend yield
};

let pricing_results = price_with_svi(svi_params, market_data, fixed_params);

println!("Calibration objective: {}", objective);
println!("SVI parameters: {:?}", params);
for result in pricing_results {
    println!("Strike {}: Model Price ${:.2}, Model IV {:.1}%", 
             result.strike_price, result.model_price, result.model_iv * 100.0);
}

Custom Model Parameters

Control the calibration weighting scheme with model-specific parameters:

use surface_lib::{CalibrationParams, SviModelParams};

// Default behavior (ATM boost = 25.0, vega weighting enabled)
let calib_params = CalibrationParams::default();

// Custom parameters for different weighting schemes
let mut calib_params = CalibrationParams::default();
calib_params.model_params = Some(Box::new(SviModelParams {
    atm_boost_factor: 15.0,        // Lower ATM emphasis (more wing weight)
    use_vega_weighting: false,     // Equal weight for all strikes
}));

let (objective, params, used_bounds) = calibrate_svi(market_data, config, calib_params, None)?;

Model Parameters:

• atm_boost_factor: Controls ATM weighting with exp(-factor * |log_moneyness|). Higher values emphasize ATM options more strongly (default: 25.0)
• use_vega_weighting: Whether to weight observations by their vega values. Set to false for equal strike weighting (default: true)

Configuration Presets

The library provides several optimization configuration presets:

use surface_lib::default_configs;

// For production trading systems
let config = default_configs::production();

// For development and testing (balanced speed/accuracy)
let config = default_configs::fast();

// For research and backtesting (highest precision)
let config = default_configs::research();

// For quick validation and debugging
let config = default_configs::minimal();

Data Structure

The library expects a MarketDataRow with these essential fields:

pub struct MarketDataRow {
    pub option_type: String,      // "call" or "put"
    pub strike_price: f64,        // Strike price
    pub underlying_price: f64,    // Underlying asset price
    pub years_to_exp: f64,        // Time to expiration in years
    pub market_iv: f64,           // Market IV as decimal (0.25 = 25%)
    pub vega: f64,                // Option vega (for weighting)
    pub expiration: i64,          // Expiration timestamp
}

SVI Model

The SVI model parameterizes total variance as:

w(k) = a + b * (ρ(k-m) + sqrt((k-m)² + σ²))

Where:

• k is log-moneyness: ln(K/S)
• a is the base variance level (vertical shift parameter)
• b is the slope factor (overall variance level)
• ρ is the asymmetry parameter (skew, must be in (-1, 1))
• m is the horizontal shift parameter (ATM location in log-moneyness)
• σ is the curvature parameter (smile curvature, must be > 0)

The model automatically enforces no-arbitrage constraints during calibration.

API Reference

Calibration

calibrate_svi(data, config, calib_params)

Calibrates SVI model parameters to market option data for a single expiration.

Arguments:

• data: Vec<MarketDataRow> - Market option data (single expiration only)
• config: OptimizationConfig - Optimization settings (use default_configs presets)
• calib_params: CalibrationParams - Calibration and model parameters
• initial_guess: Option<Vec<f64>> - Optional initial parameter guess for warm-started calibration

Returns:

• (f64, Vec<f64>, SVIParamBounds) - (objective_value, parameters, effective_parameter_bounds)

price_with_svi(params, market_data, fixed_params)

Prices European options using calibrated SVI parameters.

Arguments:

• params: SVIParams - Calibrated SVI parameters
• market_data: Vec<MarketDataRow> - Options to price
• fixed_params: FixedParameters - Risk-free rate and dividend yield

Returns:

• Vec<PricingResult> - Pricing results with model prices and implied volatilities

Model Parameters

SviModelParams

Controls SVI-specific calibration behavior:

pub struct SviModelParams {
    pub atm_boost_factor: f64,    // ATM weighting strength (default: 25.0)
    pub use_vega_weighting: bool, // Enable vega weighting (default: true)
}

Usage Examples:

• Equal weighting: SviModelParams { atm_boost_factor: 0.0, use_vega_weighting: false }
• Strong ATM focus: SviModelParams { atm_boost_factor: 50.0, use_vega_weighting: true }
• Wing emphasis: SviModelParams { atm_boost_factor: 10.0, use_vega_weighting: true }

Advanced Features

• Configurable Weighting: ATM boost and vega weighting can be independently controlled
• Auto-Bounds: Parameter bounds automatically adjust based on time to expiration
• Two-Stage Optimization: Global search with CMA-ES followed by local refinement with L-BFGS-B
• Production Ready: Optimized for real-time trading and backtesting systems

Requirements

• Data must contain options for a single expiration (SVI is a single-slice model)
• Minimum 5-10 data points recommended for stable calibration
• Market implied volatilities should be provided as decimals (0.25 for 25%)

License

Licensed under either of:

• MIT license (LICENSE-MIT)
• Apache License, Version 2.0 (LICENSE-APACHE)

at your option.