Coursera - Introduction to Computational Finance and Financial Econometrics .

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Coursera - Introduction to Computational Finance and Financial Econometrics
WEBRip | English | MP4 + Project files | 960 x 540 | AVC ~154 kbps | 30.919 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 25:23:27 | 3.86 GB
Genre: eLearning Video / Finance, Analysis, Mathematics, Statistics
Learn mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. Apply these tools to model asset returns, measure risk, and construct optimized portfolios using the open source R programming language and Microsoft Excel. Learn how to build probability models for asset returns, to apply statistical techniques to evaluate if asset returns are normally distributed, to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and to use optimization methods to construct efficient portfolios.

You'll do the R assignments for this course on DataCamp.com, an online interactive learning platform that offers free R tutorials through learning-by-doing. The platform provides you with hints and instant feedback on how to perform even better. Every week, new labs will be posted.

Course Syllabus
Topics covered include:

Computing asset returns
Univariate random variables and distributions
Characteristics of distributions, the normal distribution, linear function of random variables, quantiles of a distribution, Value-at-Risk
Bivariate distributions
Covariance, correlation, autocorrelation, linear combinations of random variables
Time Series concepts
Covariance stationarity, autocorrelations, MA(1) and AR(1) models
Matrix algebra
Descriptive statistics
histograms, sample means, variances, covariances and autocorrelations
The constant expected return model
Monte Carlo simulation, standard errors of estimates, confidence intervals, bootstrapping standard errors and confidence intervals, hypothesis testing , Maximum likelihood estimation, review of unconstrained optimization methods
Introduction to portfolio theory
Portfolio theory with matrix algebra
Review of constrained optimization methods, Markowitz algorithm, Markowitz Algorithm using the solver and matrix algebra
Statistical Analysis of Efficient Portfolios
Risk budgeting
Euler's theorem, asset contributions to volatility, beta as a measure of portfolio risk
The Single Index Model
Estimation using simple linear regression

General
Complete name : 21 - 7 - 10.12 Least Squares Estimation of Single Index Model Parameters (2106).mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom
File size : 43.9 MiB
Duration : 21mn 6s
Overall bit rate : 291 Kbps
Writing application : Lavf53.29.100

Video
ID : 1
Format : AVC
Format/Info : Advanced Video Codec
Format profile : [email protected]
Format settings, CABAC : Yes
Format settings, ReFrames : 4 frames
Codec ID : avc1
Codec ID/Info : Advanced Video Coding
Duration : 21mn 5s
Bit rate : 154 Kbps
Width : 960 pixels
Height : 540 pixels
Display aspect ratio : 16:9
Frame rate mode : Variable
Frame rate : 30.919 fps
Minimum frame rate : 30.917 fps
Maximum frame rate : 371.000 fps
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.010
Stream size : 23.3 MiB (53%)
Writing library : x264 core 120 r2120 0c7dab9
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Audio
ID : 2
Format : AAC
Format/Info : Advanced Audio Codec
Format profile : LC
Codec ID : 40
Duration : 21mn 6s
Bit rate mode : Constant
Bit rate : 128 Kbps
Channel(s) : 2 channels
Channel positions : Front: L R
Sampling rate : 44.1 KHz
Compression mode : Lossy
Delay relative to video : -2ms
Stream size : 19.3 MiB (44%)

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