Course on Statistics for Finance

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A01=Stanley L. Sclove
advanced financial statistics applications
applications in finance
applied statistics and mathematical statistics
ARIMA Model
asset return prediction
Author_Stanley L. Sclove
Bear Distributions
capital asset pricing model
Category=KFF
Category=PBT
Category=PBW
Closing Price
Conditional Expectation
Conditional VaR
Cumulative Distribution Function
Cumulative Relative Frequency
econometric techniques
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
ETF
Exponential Brownian Motion
Exponential Utility Function
Fama
FamaFrench model
financial data analysis
financial investment models
Finite Mixture Model
French model
Generalized Autoregressive Conditional Heteroscedasticity Model
HMM.
Infinite Mixture Model
intermediate-level statistics for Finance
investment risk modeling
Market Model Approach
mean-variance portfolio analysis
Minimum Variance Portfolio
models and methods from finance
Moving Average
multivariate analysis
National Academy
portfolio optimization strategies
quantitative analysts
quantitative finance methods
regression analysis
Risk Free Asset
Risk Free Rate
Seasonal ARIMA Model
Sharpe Ratio
Small Cap Portfolio
statistical methods for financial investment analysis
statistics course for finance
time series analysis
Wharton Research Data Service

Product details

  • ISBN 9780367576608
  • Weight: 440g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Jun 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Taking a data-driven approach, A Course on Statistics for Finance presents statistical methods for financial investment analysis. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance.

The book begins with a review of basic statistics, including descriptive statistics, kinds of variables, and types of data sets. It then discusses regression analysis in general terms and in terms of financial investment models, such as the capital asset pricing model and the Fama/French model. It also describes mean-variance portfolio analysis and concludes with a focus on time series analysis.

Providing the connection between elementary statistics courses and quantitative finance courses, this text helps both existing and future quants improve their data analysis skills and better understand the modeling process.

Stanley L. Sclove is a professor of statistics in the Department of Information and Decision Sciences of the College of Business Administration at the University of Illinois at Chicago (UIC). His areas of specialization within statistics include multivariate statistical analysis, cluster analysis, time series analysis, and model selection criteria. Dr. Sclove’s research interests include time series segmentation and regime switching via Markov models. He is an officer of the Classification Society and the Section of Risk Analysis of the American Statistical Association.

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