Quantitative Finance and Risk Management

 

MATLAB for Quantitative Finance and Risk Management

Import data, develop algorithms, debug code, scale up processing power, and more.

 

In just a few lines of MATLAB® code, you can prototype and validate computational finance models, accelerate those models using parallel processing, and put them directly into production.

Leading institutions use MATLAB to determine interest rates, perform stress tests, manage multi-billion dollar portfolios, and trade complex instruments in less than a second.

  • MATLAB is fast: Run risk and portfolio analytics prototypes up to 120x faster than in R, 100x faster than in Excel/VBA, and up to 64x faster than Python.
  • MATLAB automatically generates documentation for model review and regulatory approval.
  • Analysts use prebuilt apps and tools to visualize intermediate results and debug models.
  • IT groups can deploy IP protected models directly to desktop and web applications such as Excel, Tableau, Java, C++, and Python.
  • MATLAB includes an interface for importing historical and real-time market data from free and paid sources including Bloomberg, Refinitiv, FactSet, FRED, and Twitter.
  • MATLAB handles big and streaming data from traditional and alternative data sources.    

“MATLAB enabled us to concentrate on our core competencies as investment professionals and deploy a quantitative risk management and portfolio optimization dashboard that has added value from day one across our team.”

Mathew John and Jason Liddle, SMMI

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Effective Model Risk Management with MATLAB

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Investment Management

  • Build and evolve dashboards for portfolio managers, with intraday risk reporting, valuation, and trade execution capabilities. 
  • Use prebuilt tools for performing portfolio optimization using mean-variance, mean absolute deviation (MAD), conditional value-at-risk (CVaR), and Black-Litterman methods.
  • Measure investment performance using risk-adjusted alphas, tracking errors, maximum drawdowns, and the Sharpe ratio.
Investment Management

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Risk Management

Risk Management

  • Automate, augment, and provide executable reporting throughout the risk model lifecycle. Take models through model validation, model review, implementation, and regulatory approval in just three months.
  • Build risk management systems or stress testing infrastructure for CCAR, DFAST, Basel III, and Solvency II.
  • Use models and functions to quantify risk exposure (e.g., market, credit, and operational risks), validate the models using VaR and expected shortfall backtesting, and supplement the traditional methods with machine learning algorithms and text analytics.

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Algorithmic Trading

  • Develop trading strategies using traditional methods (e.g., technical indicators or econometric models) or more cutting-edge machine learning algorithms.
  • Execute trading strategies in real time using MATLAB code.
Algorithmic Trading

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Forecasting and Modeling

Financial Forecasting and Modeling 

  • Use point-and click apps to fit time-series data with econometric models (e.g., ARMA, ARIMA, GARCH, EGARCH, GJR) or machine learning algorithms.
  • Interface to DSGE models to forecast key economic variables.
  • Use functions for interest rate modeling and forecasting based on parameters estimated from the Nelson-Siegel or Svensson models.

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Derivatives Pricing

  • Calculate price and greek variables of exotic options using Monte Carlo simulation in MATLAB significantly faster than running them in Visual Basic, R, and Python.
  • Choose various pricing methods (e.g., closed-form equations, binomial trees, trinomial trees, and the stochastic volatility model) to price options. These include European options, American options, Asian options, barrier options, caps, floors, swaps, and multi-underlying asset derivatives.
  • Run compute-intensive applications in parallel or deploy them to a GPU.
  • Interface with Numerix.
Derivatives Pricing

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Actuarial Science

Insurance and Actuarial Science

  • Analyze large data sets, create custom actuarial models, and easily accelerate the simulations using parallelization.
  • Build custom risk models using MATLAB as a platform for Solvency II.
  • Price various insurance products such as variable annuities, guaranteed minimum benefit options, term assurance, and endowment policies.

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A Practical Guide: Modeling Financial Risk with MATLAB

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