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Coursera - Mathematical Methods for Quantitative Finance (University of Washington) . - aretr - 06-03-2019

[Image: 1905281224520100.jpg]

Coursera - Mathematical Methods for Quantitative Finance
WEBRip | English | MP4 | 960 x 540 | AVC ~63.8 kbps | 25 fps
AAC | 128 Kbps | 48.0 KHz | 2 channels | ~8 hours | 5.98 GB
Genre: eLearning Video / Maths, Economics & Finance
Mathematical Methods for Quantitative Finance covers topics from calculus and linear algebra that are fundamental for the study of mathematical finance. Students successfully completing this course will be mathematically well prepared to study quantitative finance at the graduate level.

The Mathematical Methods for Quantitative Finance course reviews the mathematical methods fundamental for the study of quantitative and computational finance. The areas of focus include calculus and multivariable calculus, constrained and unconstrained optimization, and linear algebra.

Topics covered include the following:

Functions and inverse functions
Limits, derivatives, partial derivatives, and chain rule
Integrals and multiple integrals, changing the order of differentiation and integration
Taylor series approximations
Newton's method
Lagrange multiplier method
Vector and matrix arithmetic, determinants, eigenvalue-eigenvector decomposition, singular value decomposition
Numerical methods for optimization

Course goal:

Upon completion of the course students will know the fundamental mathematical concepts needed to effectively study quantitative finance areas such as fixed income, options and derivatives, portfolio optimization, and quantitative risk management.

Course Objectives:

Upon completion of the course students will:

Understand the concept of a limit, differentiation, and integration;
Be able to compute partial derivatives and multiple integrals;
Understand the utility of matrix decompositions;
Be able to use Lagrange multipliers to solve constrained optimization problems; and
Apply the above methods to problems arising in finance.

General
Complete name : Mathematical Methods for Quantitative Finance 8.1 W7.1.1 - Optimal Investment Portfolios (1732).mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom
File size : 24.8 MiB
Duration : 17mn 32s
Overall bit rate mode : Variable
Overall bit rate : 198 Kbps
Movie name : MMfQF_W7_P1_Kjell_OptimalInvestmentPortfolios_FINAL
Composer : Jon Keib
Writing application : Lavf52.99.1
desc : This video is about MMfQF_W7_P1_Kjell_OptimalInvestmentPortfolios

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 : 17mn 32s
Bit rate : 63.8 Kbps
Width : 960 pixels
Height : 540 pixels
Display aspect ratio : 16:9
Frame rate mode : Constant
Frame rate : 25.000 fps
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.005
Stream size : 8.00 MiB (32%)
Writing library : x264 core 114 r1913 5fd3dce
Encoding settings : cabac=1 / ref=1 / deblock=1:0:0 / analyse=0x3:0x113 / me=hex / subme=2 / psy=1 / psy_rd=0.00:0.00 / mixed_ref=0 / me_range=16 / chroma_me=1 / trellis=0 / 8x8dct=1 / cqm=0 / deadzone=21,11 / fast_pskip=1 / chroma_qp_offset=0 / threads=3 / sliced_threads=0 / nr=0 / decimate=1 / interlaced=0 / constrained_intra=0 / bframes=3 / b_pyramid=2 / b_adapt=1 / b_bias=0 / direct=1 / weightb=1 / open_gop=0 / weightp=0 / keyint=250 / keyint_min=25 / scenecut=40 / intra_refresh=0 / rc_lookahead=10 / rc=crf / mbtree=1 / crf=28.0 / qcomp=0.60 / qpmin=0 / qpmax=69 / qpstep=4 / ip_ratio=1.41 / aq=1:1.00
Language : English

Audio
ID : 2
Format : AAC
Format/Info : Advanced Audio Codec
Format profile : LC
Codec ID : 40
Duration : 17mn 32s
Bit rate mode : Variable
Bit rate : 128 Kbps
Channel(s) : 2 channels
Channel positions : Front: L R
Sampling rate : 48.0 KHz
Compression mode : Lossy
Delay relative to video : -2ms
Stream size : 16.0 MiB (65%)
Language : English

Screenshots

[Image: 1905281224530101.jpg]

[Image: 1905281224550109.jpg]

[Image: 1905281224560112.jpg]
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