Applied Finance in R

Zaktualizowano 03.2026

Grow your financial skills in R. Learn how to evaluate portfolios, calculate credit risk, and create GARCH models to forecast volatility.

Utwórz bezpłatne konto

Lub

Kontynuując, akceptujesz nasze Warunki korzystania, naszą Politykę prywatności oraz fakt, że Twoje dane są przechowywane w USA.

Opis utworu

Applied Finance in R

Grow your financial skills in R and learn how to manipulate data and make better data-driven decisions.You’ll begin this track by learning how to evaluate portfolios and value stocks using present value approaches, free cash flow, and equity and dividend discount models. Next, you’ll explore life insurance products and learn the basics of financial trading. Through interactive coding exercises, you’ll use powerful libraries, including quantmod, QRM, xts, zoo, and quantstrat, to examine and manage credit risk. You’ll then apply what you’ve learned to answer questions commonly faced by financial firms, such as how to value a fixed interest rate bond and estimate a bond's yield. Along the way, you’ll also create GARCH models and get hands-on with real data from the US Treasury, daily Microsoft returns, and S&P 500 data. Start this track to advance your R financial skills.

Wymagania wstępne

Nie ma żadnych wymagań wstępnych dla tego toru

Course
1
Quantitative Risk Management in R
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Course
2
Equity Valuation in R
Learn the fundamentals of valuing stocks.
Course
4
Bond Valuation and Analysis in R
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Course
7
Financial Trading in R
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Course
8
Credit Risk Modeling in R
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Course
9
GARCH Models in R
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.