Learn about modeling financial data from quantitative finance expert Jonathan Kinlay. Stochastic processes in this course include random walks, Wiener processes and geometric Brownian motion.
Summary
Learn about modeling financial data with stochastic processes for quantitative research and trading from an expert in quantitative finance. Instructor Jonathan Kinlay is a longtime user of Wolfram Language and an expert in investment research, hedge fund investing and quantitative finance. Analysts and portfolio managers in the financial sector often rely on computational models for managing assets and optimizing investment portfolios. Modeling stochastic processes helps you make sense of and predict outcomes in random data, such as stock prices. The stochastic processes in this course include a random walk, the Wiener process and geometric Brownian motion. The course will cover the models' properties and applications in analyzing financial data. Examples demonstrate modeling a stock Index and stock returns, as well as option pricing and sensitivity analysis.
Featured Products & Technologies:
Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
Model random walks and compute moments
Calculate the PDF and CDF of a random walk process
Model a Wiener process and compute its moments and distribution properties
Explore stationarity and independence properties of random processes
Simulate stock price paths using geometric Brownian motion
Model stock returns
Derive option pricing formula from the PDF of stock price
Summary
Learn about modeling financial data with stochastic processes for quantitative research and trading from an expert in quantitative finance. Instructor Jonathan Kinlay is a longtime user of Wolfram Language and an expert in investment research, hedge fund investing and quantitative finance. Analysts and portfolio managers in the financial sector often rely on computational models for managing assets and optimizing investment portfolios. Modeling stochastic processes helps you make sense of and predict outcomes in random data, such as stock prices. The stochastic processes in this course include a random walk, the Wiener process and geometric Brownian motion. The course will cover the models' properties and applications in analyzing financial data. Examples demonstrate modeling a stock Index and stock returns, as well as option pricing and sensitivity analysis.
Featured Products & Technologies:
Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
Model random walks and compute moments
Calculate the PDF and CDF of a random walk process
Model a Wiener process and compute its moments and distribution properties
Explore stationarity and independence properties of random processes
Simulate stock price paths using geometric Brownian motion
Model stock returns
Derive option pricing formula from the PDF of stock price
