Matlab for Finance Kurzusok

Kurzuskód

matlabfincance

Duration

14 hours (usually 2 days including breaks)

Követelmények

  • 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.

Overview

A MATLAB a számítást, a vizualizálást és a programozást integrálja egy könnyen használható környezetben A pénzügyi eszközkészletet tartalmazza, amely magában foglalja a pénzügyi adatok matematikai és statisztikai elemzéséhez szükséges jellemzőket, majd megjeleníti az eredményeket bemutató minőségű grafikával Ez az oktatott képzés bemutatja a MATLAB számára a finanszírozást Az adatelemzés, a vizualizáció, a modellezés és a programozás során merülünk fel handson gyakorlatok és bőséges inlab gyakorlat révén A képzés befejeztével a résztvevők mélyreható megértést kapnak a MATLAB pénzügyi eszköztárában szereplő erőteljes jellemzőkkel, és megszerezték a szükséges gyakorlatot, hogy azonnal alkalmazzák őket a valós problémák megoldására Közönség Pénzügyi szakemberek, akik korábbi tapasztalattal rendelkeznek a MATLAB-kal A tanfolyam formátuma Részelőadás, részbeszélgetés, nehéz handson gyakorlat .

Kurzusleírás

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

Ajánlások

★★★★★
★★★★★

Course Discounts

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