Quantitative Finance and Economics Lecture 9: Portfolio Theory

This lecture of quantitative finance and economics covers portfolio theory.

1  Lecture Slides 

Download PDF slides: Introduction to portfolio theory

Download PDF slides: Portfolio Theory with Matrix

Portfolio Theory Examples

Portfolio Theory with Matrices Examples

R Portfolio Functions

IntroPortfolioTheory.xls

R codes: portfolio.r , testport.r

 

2  Introduction (2:57)

 

3  Introduction to Portfolio Theory (14:35)

 

4  Portfolio Examples (6:08)

 

5  Portfolio Value-at-Risk (6:11)

 

6  Portfolio Frontier (10:28)

 

7  Efficient Portfolios (10:00)

 

8  Minimum Variance Portfolio (12:43)

 

9  Portfolios with a Risk Free Asset, Part_1 (7:24)

 

10  Portfolios with a Risk Free Asset, Part_2 (18:32)

 

11  Tangency Portfolio (17:33)

 

12  Examples (10:11)

 

13  Portfolio Theory with Matrix Algebra, Part 1 (15:26)

 

14  Portfolio Theory with Matrix Algebra, Part 2 (15:54)

 

15  Portfolio Theory with Matrix Algebra, Part 3 (16:34)

 

16  Brief Comment about Excel Solver Add-in (2:12)

 

 

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Quantitative Finance and Economics Lecture 8: Hypothtesis Testing

This lecture of quantitative finance and economics covers hypothesis testing.

1  Lecture Slides 

Download PDF slides

R Hypothesis Testing Examples

R codes: hypothesisTestingCER.r

 

2  Hypothesis testing: Introduction (8:29)

 

3  Hypothesis testing: Overview (9:06)

 

4  Hypothesis testing: CER Model (10:47)

 

5  Chi-square and Student’s t distributions (5:16)

 

6  Test of Specific Coefficient Value (26:07)

 

7  Test for Normal Distribution (8:36)

 

8  Test for No Autocorrelation (5:36)

 

9  Diagnostics for Constant Parameters (22:21)

 

 

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Quantitative Finance and Economics Lecture 7: Bootstrapping

This lecture of quantitative finance and economics covers bootstrapping.

1  Lecture Slides 

Download PDF slides

R Bootstrap Examples

R codes: bootStrap.r

 

2  Introduction (2:43)

 

3  Bootstrap (26:06)

 

4  Performing Bootstrapping in R (18:10)

 

5  Bootstrapping VaR (8:44)

 

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Quantitative Finance and Economics Lecture 6: Constant Expected Return Model and Estimation

This lecture of quantitative finance and economics covers constant expected return model and estimation.

1  Lecture Slides 

Download PDF slides

R CER Model Examples

cerExample.csv

R codes: cerModelExamples.r

 

2  Introduction (11:28)

 

3  Constant Expected Return Model (14:07)

 

4  Simulating Data (12:14)

 

5  Random Walk Model (5:38)

 

6  Estimateing Parameters of CER (18:59)

 

 

7  Bias and Precision (13:02)

 

8  Mean Squared Error (1:22)

 

9  Standard Error (22:12)

 

10  Asymptotic Properties of Estimators (14:11)

 

11  Confidence Intervals (12:47)

 

12  Monte Carlo Simulation (15:27)

 

13  Value at Risk in CER Model (7:36)

 

 

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Financial Engineering Lecture 4: Options Pricing by Multi Period Binomial Model

This lecture covers options pricing by multi period binomial model.

1  The multi-period binomial model

Lecture slides

Excel spreadsheet: BinomialModel_Public.xlsx

 

2  What’s going on?

Lecture slides

 

3  Pricing American options

Lecture slides

 

4  Replicating strategies

Lecture slides

 

5  Including dividends

Lecture slides

 

6  Pricing forwards and futures

Lecture slides

 

7  The Black-Sholes model

Lecture slides

 

8  An Example: Pricing a European Put on a Futures Contract

Lecture slides

 

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Financial Engineering Lecture 2: Basic Fixed Income Securities

,,,,     This lecture presents an introduction to basic fixed income securities.

1  Basics of fixed income securities

1.1  Introduction to no-arbitrage

Lecture slides

 

1.2  Interest rates and fixed income instruments

Lecture slides

 

 

 

2  Basic fixed income instruments

2.1  Floating rate bonds and term structure of interest rates

Lecture slides

 

 

2.2  Forward contracts

Lecture slides

 

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Financial Engineering Lecture 1: Course Overview

This lecture is the overview of this course of financial engineering.

Overview

Financial Engineering is a multidisciplinary field drawing from finance and economics, mathematics, statistics, engineering and computational methods. The emphasis of FE & RM Part I, lectures 1~10, will be on the use of simple stochastic models to price derivative securities in various asset classes including equities, fixed income, credit and mortgage-backed securities. We will also consider the role that some of these asset classes played during the financial crisis. A notable feature of this course will be an interview module with Emanuel Derman, the renowned “quant” and best-selling author of “My Life as a Quant.” We hope that students who complete the course will begin to understand the “rocket science” behind financial engineering but perhaps more importantly, we hope they will also understand the limitations of this theory in practice and why financial models should always be treated with a healthy degree of skepticism. The follow-on course FE & RM Part II, lectures 11~20, will continue to develop derivatives pricing models but it will also focus on asset allocation and portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Read more Financial Engineering Lecture 1: Course Overview

Quantitative Finance and Economics Lecture 5: Descriptive Statistics

This lecture of quantitative finance and economics covers descriptive statistics.

1  Lecture Slides 

Download PDF slides

R Descriptive Statistics Examples

R Examples: Descriptive Statistics Examples for Daily Dataownload PDF slides

R codes: descriptiveStatistics.r

 

2  Covariance Stationarity (11:28)

 

3  Histograms (11:33)

 

4  Sample Statistics (15:24)

 

5  Empirical CDF and QQ plots (12:00)

 

6  Outliers Part 1 (7:15)

 

7  Outliers Part 2 (7:39)

 

8  Descriptive Statistics for Daily Data (24:17)

 

 

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Quantitative Finance and Economics Lecture 4: Time Series Conceipts

This lecture of quantitative finance and economics covers some basic conceipts of time series.

1  Lecture Slides 

Download PDF slides

R Time Series Examples

R codes: timeSeriesConcepts.r

 

2  Time Series Concepts (16:48)

 

3  Autocorrelation (9:14)

 

4  White Noise Processes (12:31)

 

5  Nonstationary Processes (17:29)

 

6  Moving Average Processes (25:45)

 

7  Autoregressive Processes Part 1 (3:19)

 

8  Autoregressive Processes Part 2 (28:19)

 

 

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