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Financial Derivatives: A Quantitative Finance View

Posted By: ELK1nG
Financial Derivatives: A Quantitative Finance View

Financial Derivatives: A Quantitative Finance View
Last updated 11/2022
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.25 GB | Duration: 27h 15m

The financial engineering of forwards, futures, swaps, and options, with Python tools for fixed income and options

What you'll learn

Learn the fundamentals of derivatives at a quantitative level

Master arbitrage, the core principle underlying derivatives, quantitative risk management and quantitative trading

Use derivatives to control and manage financial risk

Price forwards, futures, swaps and options

Understand the Black-Scholes theory and formula intuitively, avoiding stochastic calculus

Learn the limitations of the Black-Scholes theory, and how it is used in practice

Python based tools are provided for computations with bonds, yield curves, and options


Calculus and a basic course in probability and statistics

No knowledge or background in finance is assumed


Student Testimonials:This course offers an unreal value. Very rich content! This beats any financial course I've taken at my university. Looking forward to completing this course and using some of these skills in my career.–StevenCameron is an outstanding teacher. Thank you very much for making the most important and difficult Finance concepts so easy to understand. Looking forward to the further courses.–GevorgI got (am getting) some intuition about quant finance, not just leaning facts without really understanding the concepts.Cameron gives nice detailed answers to students questions.–RichInterested in a lucrative and rewarding position in quantitative finance?  Are you a quantitative professional working in finance or a technical field and want to bridge the gap and become a full on quant?  Then read on.The role of a quantitative analyst in an investment bank, hedge fund, or financial company is an attractive career option for many quantitatively skilled professionals working in finance or other fields like data science, technology or engineering.  If this describes you, what you need to move to the next level is a gateway to the quantitative finance knowledge required for this role that builds on the technical foundations you have already mastered.This course is designed to be exactly such a gateway into the quant world.  If you succeed in this course you will become a master of quantitative finance and the financial engineering of the most influential class of financial products that exist on markets today: derivatives.About the instructor:This course was created by a mathematician and financial quant holding a Ph.D. from the Courant Institute of Mathematical Sciences at NYU, and who earned his quant chops on Wall Street after an accomplished career as a theoretical materials scientist.The focus of the course is thus very much on the practical skills someone working in the trenches in the real world of finance needs to have.  But since the course author also has 10 years of college teaching experience, it is taught with an eye to sound course structure and sensitivity to the concerns of students.What you will learn:Many finance students and professionals find derivatives the most challenging subject in their field.  But if you have a background in quantitative fields like statistics or computer science this course will show you that these most daunting of financial products are completely accessible to you.Even if you are completely new to the world of finance, after completing this course you will have a deep mastery of the fundamental derivative structures traded on markets today: forwards, futures, swaps, and options.  But since this course is presented by a practitioner you will also learn how derivatives are actually used in the real world, as tools for both speculation and risk management.The world of finance and markets is fast-paced and exciting, but can also be very intimidating.  In the heat of the moment, the markets are volatile and unpredictable, positions go south in unanticipated ways, you have traders yelling at you, you have computer software failing, you're relying on data you can't trust.  Keeping your head above water in this environment can be well nigh impossible.You need a conceptual framework that allows you to keep above the fray and keep your wits about you.  In this course, my primary purpose is to convey that conceptual framework to my students.  The same conceptual framework that allowed me to survive and thrive in the pits of Wall Street during the dark days of the financial crisis.Concerned that you may not have the required background to succeed in this course?  As long as you meet the formal prerequisites you need not be.  A quantitatively strong business background is more than enough to meet these requirements.  Any decent course in statistics and the basics of calculus is enough.  In truth, high school mathematics is all that is needed for 80-90% of the course material.  The most important requirement is simply to think analytically and logically.Here is a sampling of some of the main topics that we'll cover on your journey into the quant profession:Interest rate fundamentalsPeriodic and continuous compoundingDiscounted cash flow analysisBond analysisThe fundamentals of equity, currency, and commodity assetsPortfolio modellingLong and short positionsThe principle of arbitrageThe Law of One PriceForwards, futures, and swapsRisk management principlesFutures hedgingStochastic processesTime series conceptsThe real statistics of asset prices: volatility clustering and autocorrelationFat-tailed distribution and their importance for financial assetsBrownian motionThe log-normal model of asset pricesOptionsPut-call parityThe binomial model of option pricingThe Black-Scholes theory and formulaOption greeks: delta, gamma, and vegaDynamic hedgingVolatility tradingImplied volatilityIncludes Python toolsPython based tools are now included for computations with bonds, yield curves, and options.  All software that is part of this course is released under a permissive MIT license, so students are free to take these tools with them and use them in their future careers, include them in their own projects, whether open source or proprietary, anything you want!So Sign Up Now!Accelerate your finance career by taking this course, and advancing into quantitative finance.  With 23 hours of lectures and supplemental course materials including 10 problem sets and solutions, the course content is equivalent to a full semester college course, available for a fraction of that price, not to mention a 30 day money back guarantee.  You can't go wrong!


Section 1: Introduction

Lecture 1 Introduction

Section 2: Fundamentals

Lecture 2 Interest Rates

Lecture 3 Interest Rates: General Considerations

Lecture 4 Interest Rates and Future Values

Lecture 5 Compounding Conventions

Lecture 6 Investment Return Measures

Lecture 7 Interest Rate Conversions

Lecture 8 Continuous Compounding

Lecture 9 The Time Value of Money

Lecture 10 Present Value

Lecture 11 Discount Factors

Lecture 12 Discounted Cash Flow Analysis

Lecture 13 Bonds and Discounted Cash Flow Analysis

Lecture 14 Yield to Maturity

Lecture 15 Python Tools: Bonds

Lecture 16 Simple Interest and Day Count Conventions

Lecture 17 LIBOR

Lecture 18 Fed Funds Rate

Lecture 19 SONIA: The Sterling Overnight Index Average

Lecture 20 SOFR: The Secured Overnight Financing Rate

Lecture 21 Yield Curves and Discount Curves

Lecture 22 Python Tools: Yield Curves I

Lecture 23 Bootstrapping Spot Curves from Bonds

Lecture 24 Bootstrapping Spot Curves from Bonds II

Lecture 25 Python Tools: Yield Curves II

Lecture 26 Interest Rates: Default Assumptions

Lecture 27 Equity Assets: Stock

Lecture 28 Commodities

Lecture 29 Modelling Portfolios

Lecture 30 Foreign Currencies

Lecture 31 Dividends, Convenience Yields, and Storage

Lecture 32 Long and Short Positions

Lecture 33 Long/Short Example

Section 3: Arbitrage

Lecture 34 The Arbitrage Concept

Lecture 35 Arbitrage: Formal Definition

Lecture 36 Arbitrage Example #1

Lecture 37 Arbitrage Example #2

Lecture 38 The Law of One Price

Lecture 39 Law of One Price: Extensions and Examples

Lecture 40 Arbitrage and Discounted Cash Flow Analysis

Section 4: Forwards, Futures, and Swaps

Lecture 41 Derivatives

Lecture 42 Derivative Markets

Lecture 43 Forward Contracts

Lecture 44 Forward Payoffs

Lecture 45 Pricing Forward Contracts

Lecture 46 The Cash and Carry Arbitrage

Lecture 47 Forward Example: A Zero Coupon Bond

Lecture 48 Forward Example: A Stock (No Dividends)

Lecture 49 Forwards on Assets Paying a Known Income

Lecture 50 Forward Valuation with Known Income

Lecture 51 Forwards on Assets Paying a Known Yield

Lecture 52 Forward Example: A Dividend Paying Stock

Lecture 53 FX Forwards

Lecture 54 FX Forward Examples

Lecture 55 Futures Contracts

Lecture 56 Futures Prices

Lecture 57 Futures Marking to Market

Lecture 58 Futures: Margin Accounts

Lecture 59 Futures Prices and Spot Prices

Lecture 60 Convergence of Futures Prices to Spot Prices

Lecture 61 Futures Contracts and Cash Exposures

Lecture 62 Futures Hedging

Lecture 63 Futures Hedging Example #1

Lecture 64 Futures Hedging and Basis Risk

Lecture 65 Futures Hedging Example #2

Lecture 66 Futures Hedging Example #3

Lecture 67 Speculation and Leverage with Futures

Lecture 68 A Futures Speculating Example

Lecture 69 The LIBOR Spot Curve

Lecture 70 Forward Interest Rates

Lecture 71 Forward Rate Agreements

Lecture 72 FRA Valuation

Lecture 73 Eurodollar Futures

Lecture 74 Swaps

Lecture 75 Pricing Swaps

Lecture 76 Swap Example #1

Lecture 77 Swap Example #2

Lecture 78 Building a LIBOR Curve: Overview

Lecture 79 Building a LIBOR Curve: the Short End

Lecture 80 Building a LIBOR Curve: the Midrange

Lecture 81 Building a LIBOR Curve: the Long End

Lecture 82 Python Tools: Yield Curves III

Section 5: Stochastic Processes and Asset Prices

Lecture 83 Stochastic Processes: The Fundamental Idea

Lecture 84 Stochastic Processes: Formalities

Lecture 85 Time Series Statistics

Lecture 86 Fat-Tailed Distributions

Lecture 87 Asset Return Measures

Lecture 88 The Stylized Facts of Asset Prices

Lecture 89 Volatility Clustering

Lecture 90 Asset Return Autocorrelation

Lecture 91 Fat Tails of Asset Returns

Lecture 92 Random Walks

Lecture 93 The Distribution of Random Walks

Lecture 94 Random Walks as Models for Asset Prices

Lecture 95 Random Walks and Efficient Markets

Lecture 96 Brownian Motion

Lecture 97 Brownian Motion with Drift

Lecture 98 Brownian Motion and Asset Prices

Lecture 99 The Log-Normal Model

Lecture 100 The Log-Normal Model and Asset Prices

Section 6: Options

Lecture 101 Options

Lecture 102 Option Payoffs

Lecture 103 Arbitrage Bounds on Options: Geometry

Lecture 104 Arbitrage Bounds on Option Prices

Lecture 105 Arbitrage Inequality #1

Lecture 106 Arbitrage Inequality #3

Lecture 107 Extensions and Applications of Option Bounds

Lecture 108 Bounds on American Options

Lecture 109 The Geometry of Put-Call Parity

Lecture 110 Put-Call Parity

Lecture 111 The Binomial Model: 1 Step

Lecture 112 The 1 Step Binomial Model: The General Case

Lecture 113 1 Step Risk Neutral Pricing

Lecture 114 A 1 Step Risk Neutral Pricing Example

Lecture 115 The Binomial Model: 2 Steps

Lecture 116 The Distribution in the 2 Step Binomial Model

Lecture 117 The Full Binomial Model

Lecture 118 Call Pricing in the Binomial Model

Lecture 119 Binomial Approximation to a Log-Normal

Lecture 120 The Black-Scholes Formula

Lecture 121 Flaws of the Black-Scholes Theory

Lecture 122 The Black-Scholes Theory in Practice

Lecture 123 Option Greeks

Lecture 124 Option Theta and Time Decay

Lecture 125 Python Tools: Options

Lecture 126 Dynamic Hedging and Delta Neutral Trading

Lecture 127 Options and Volatility Trading

Lecture 128 Implied Volatility

Technical professionals who want to learn about quantitative finance,Finance professionals who want to improve their quantitative skills and learn how to analyze derivative products