Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative
Models Example
let model = BlackScholesModel; // BinomialTreeModel OR BlackScholesModel GarchModel MonteCarloModel ...
let params = OptionParameters {
s: opts.s,
k: opts.k,
r: opts.r,
sigma: opts.sigma,
t: opts.t,
};
let call_price = model.call_price(¶ms);
let put_price = model.put_price(¶ms);Strategies Example
fn test_dance() {
let model = BlackScholesModel;
let params1 = OptionParameters {
s: 100.0,
k: 90.0,
r: 0.05,
sigma: 0.2,
t: 0.5,
};
let params2 = OptionParameters {
s: 100.0,
k: 100.0,
r: 0.05,
sigma: 0.2,
t: 0.5,
};
let params3 = OptionParameters {
s: 100.0,
k: 110.0,
r: 0.05,
sigma: 0.2,
t: 0.5
};
let dance = Dance::new(&model, params1, params2, params3);
let price = dance.price();
assert!(price > 0.0 && price < 100.0);
}Used for option pricing by constructing a binomial tree to represent possible paths an asset's price could take over time. It is particularly useful for valuing American options, which can be exercised at any time before expiration.
Used model for pricing European options. It assumes that the price of the underlying asset follows a geometric Brownian motion with constant volatility and interest rate. The model provides a closed-form solution for option pricing.
Used to value options by simulating a large number of possible price paths for the underlying asset. It is particularly useful for valuing complex derivatives and options with path-dependent features, as it can accommodate various stochastic processes and payoff structures.
Used for modeling financial time series data that exhibit volatility clustering. It extends the ARCH model by allowing past variances to influence current variances, providing a more flexible approach to volatility modeling.
Click More 100+ Model
| Model Name | Description |
|---|---|
| AARCH | Handles asymmetric volatility in time series |
| DVEC-GARCH | Uses diagonal vector model to handle multivariate data volatility |
| GARJI | Combines GARCH model with jumps to capture sudden price changes |
| MS-GARCH | Combines Markov state switching with GARCH model |
| SPARCH | Handles smooth transitions in volatility |
| ADCC-GARCH | Handles asymmetric dynamic conditional correlation |
| EGARCH | Uses exponential function to handle asymmetric volatility |
| GDCC-GARCH | A generalized dynamic conditional correlation model |
| MV-GARCH | Handles multivariate data volatility |
| Spline-GARCH | Uses spline functions to model volatility |
| AGARCH | An adjusted GARCH model for better fit |
| EVT-GARCH | Incorporates extreme value theory into GARCH modeling |
| GED-GARCH | Uses Generalized Error Distribution for modeling |
| NAGARCH | Nonlinear asymmetric GARCH model |
| SQR-GARCH | Uses squared returns in GARCH model |
| ANN-ARCH | Uses artificial neural networks with ARCH model |
| F-ARCH | Fractionally integrated ARCH model |
| GJR-GARCH | Threshold GARCH model that captures leverage effect |
| NGARCH | Nonlinear GARCH model |
| STARCH | Smooth transition ARCH model |
| ANST-GARCH | Asymmetric nonlinear smooth transition GARCH model |
| FDCC-GARCH | Flexible dynamic conditional correlation GARCH model |
| GO-GARCH | Generalized orthogonal GARCH model |
| NL-GARCH | Nonlinear GARCH model |
| Stdev-ARCH | Standard deviation ARCH model |
| APARCH | Asymmetric power ARCH model |
| FGARCH | Flexible GARCH model |
| GQARCH | Quadratic GARCH model |
| NM-GARCH | Nonparametric GARCH model |
| STGARCH | Smooth transition GARCH model |
| ARCH-M | ARCH-in-mean model |
| FIAPARCH | Fractionally integrated asymmetric power ARCH model |
| GQTARCH | Generalized quadratic ARCH model |
| OGARCH | Orthogonal GARCH model |
| Structural GARCH | Models structural changes in volatility |
| ARCH-SM | Stochastic mean ARCH model |
| FIEGARCH | Fractionally integrated EGARCH model |
| HARCH | Hierarchical ARCH model |
| PARCH | Power ARCH model |
| Strong GARCH | Robust GARCH model |
| ATGARCH | Adaptive threshold GARCH model |
| FIGARCH | Fractionally integrated GARCH model |
| HGARCH | Heteroscedastic GARCH model |
| PC-GARCH | Principal component GARCH model |
| SWARCH | Switching ARCH model |
| Aug-GARCH | Augmented GARCH model |
| FIREGARCH | Fractionally integrated random effects GARCH model |
| HYGARCH | Hyperbolic GARCH model |
| PGARCH | Polynomial GARCH model |
| TGARCH | Threshold GARCH model |
| AVGARCH | Average GARCH model |
| Flex-GARCH | Flexible GARCH model |
| IGARCH | Integrated GARCH model |
| PNP-GARCH | Penalized nonparametric GARCH model |
| t-GARCH | Student-t GARCH model |
| B-GARCH | Bayesian GARCH model |
| GAARCH | Generalized asymmetric ARCH model |
| LARCH | Linear ARCH model |
| QARCH | Quadratic ARCH model |
| Tobit-GARCH | Tobit GARCH model |
| BEKK-GARCH | Baba, Engle, Kraft and Kroner GARCH model |
| GARCH-Delta | Delta GARCH model |
| Latent GARCH | Latent variable GARCH model |
| QTARCH | Quantile threshold ARCH model |
| TS-GARCH | Time series GARCH model |
| CCC-GARCH | Constant conditional correlation GARCH model |
| GARCH Diffusion | Diffusion GARCH model |
| Level GARCH | Level shift GARCH model |
| REGARCH | Robust and efficient GARCH model |
| UGARCH | Univariate GARCH model |
| Censored-GARCH | Censored GARCH model |
| GARCH-EAR | GARCH model with expected average returns |
| LGARCH | Logarithmic GARCH model |
| RGARCH | Robust GARCH model |
| VCC-GARCH | Varying coefficient correlation GARCH model |
| CGARCH | Component GARCH model |
| GARCH-Gamma | GARCH model with gamma distribution |
| LMGARCH | Log-mean GARCH model |
| Robust GARCH | Robust GARCH model |
| VGARCH | Vector GARCH model |
| COGARCH | Continuous-time GARCH model |
| GARCH-M | GARCH-in-mean model |
| Log-GARCH | Logarithmic GARCH model |
| Root GARCH | Root GARCH model |
| VSGARCH | Volatility spillover GARCH model |
| CorrARCH | Correlation ARCH model |
| GARCHS | Seasonal GARCH model |
| MAR-ARCH | Multivariate ARCH model |
| RS-GARCH | Regime switching GARCH model |
| Weak GARCH | Weak GARCH model |
| DAGARCH | Diagonal ARCH model |
| GARCHSK | GARCH model with skewness |
| MARCH | Moving average ARCH model |
| Robust DCC-GARCH | Robust dynamic conditional correlation GARCH model |
| ZARCH | Zero-inflated ARCH model |
| DCC-GARCH | Dynamic conditional correlation GARCH model |
| GARCH-t | GARCH model with t-distribution |
| Matrix EGARCH | Matrix exponential GARCH model |
| SGARCH | Seasonal GARCH model |
| Diag MGARCH | Diagonal multivariate GARCH model |
| GARCH-X | GARCH model with exogenous variables |
| MGARCH | Multivariate GARCH model |
| S-GARCH | Smooth GARCH model |
| DTARCH | Double threshold ARCH model |
| GARCHX | GARCH model with explanatory variables |
| Mixture GARCH | Mixture of GARCH models |
| Sign-GARCH | GARCH model with sign-dependent effects |
Contributions are welcome! Please open an issue or [submit PR ] (https://github.com/Liberxue/cqf/pulls) for any improvements or new features.
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