Matlab for Finance Treningskurs

Last updated

Kurskode

matlabfincance

Varighet

14 timer (vanligvis 2 dag inkludert pauser)

Krav

  • Familiarity with linear algebra (i.e., matrix operations)
  • Familiarity with basic statistics
  • Understanding of financial principles
  • Understanding of MATLAB fundamentals

Course options

  • If you wish to take this course, but lack experience in MATLAB (or need a refresher), this course can be combined with a beginner's course and provided as: MATLAB Fundamentals + MATLAB for Finance.
  • If you wish to adjust the topics covered in this course (e.g., remove, shorten, or lengthen coverage of certain features), please contact us to arrange.

Oversikt

MATLAB integrerer beregning, visualisering og programmering i et brukervennlig miljø. Det tilbyr Financial Toolbox, som inkluderer funksjonene som er nødvendige for å utføre matematisk og statistisk analyse av finansielle data, og deretter viser resultatene med presentasjon-kvalitet grafikk.

Denne instruktørledede opplæringen gir en introduksjon til MATLAB for økonomi. Vi dyker inn i dataanalyse, visualisering, modellering og programmering gjennom praktiske øvelser og omfattende laboratoriepraksis.

Ved slutten av denne treningen, vil deltakerne ha en grundig forståelse av de kraftige funksjonene som er inkludert i MATLAB's Financial Toolbox og vil ha fått den nødvendige praksis å anvende dem umiddelbart for å løse virkelige problemer.

Publikum

    Finansielle fagfolk med tidligere erfaring med MATLAB

Format av kurset

    Del leksjon, del diskusjon, tung praksis

Machine Translated

Kursplan

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

  • Asset Allocation and Portfolio Optimization
  • Risk Analysis and Investment Performance
  • Fixed-Income Analysis and Option Pricing
  • Financial Time Series Analysis
  • Regression and Estimation with Missing Data
  • Technical Indicators and Financial Charts
  • Monte Carlo Simulation of SDE Models

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

  • Estimating asset return and total return moments from price or return data
  • Computing portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR)
  • Performing constrained mean-variance portfolio optimization and analysis
  • Examining the time evolution of efficient portfolio allocations
  • Performing capital allocation
  • Accounting for turnover and transaction costs in portfolio optimization problems

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

  • Specifying a portfolio name, the number of assets in an asset universe, and asset identifiers.
  • Defining an initial portfolio allocation.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

  • Analyzing cash flow
  • Performing SIA-Compliant fixed-income security analysis
  • Performing basic Black-Scholes, Black, and binomial option-pricing

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

  • Performing data math
  • Transforming and analyzing data
  • Technical analysis
  • Charting and graphics

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

  • Performing common regressions
  • Estimating log-likelihood function and standard errors for hypothesis testing
  • Completing calculations when data is missing

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

  • Moving averages
  • Oscillators, stochastics, indexes, and indicators
  • Maximum drawdown and expected maximum drawdown
  • Charts, including Bollinger bands, candlestick plots, and moving averages

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

  • Brownian Motion (BM)
  • Geometric Brownian Motion (GBM)
  • Constant Elasticity of Variance (CEV)
  • Cox-Ingersoll-Ross (CIR)
  • Hull-White/Vasicek (HWV)
  • Heston

Conclusion

Testimonials

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