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# Math for Quantitative Finance

## Tour the mathematics used to model the chaos of the financial markets.

This course was written in collaboration with former quantitative traders from two leading firms.

In this course, we'll dive into statistical modeling, matrices, and Markov chains, and guide you through the powerful mathematics and statistics used to model the chaos of the financial markets.

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1. 1

### Intro to Quant Finance

See why math is the new hero of finance.

1. #### Financial Models

The new heroes of trading and finance are math, statistics, and computer science.

2. #### Probability

Probability is the cornerstone of quantitative financial modeling.

3. #### Value and Risk

Learn how to account for risk when making quantitative decisions.

2. 2

### Probability

1. Included with

#### Probability Warm-ups

Practice the problem-solving skills required for tackling challenging probability questions.

2. Included with

#### Conditional Probability

In a fast-paced market, here's how to update your beliefs in light of new information.

3. Included with

#### Interview Preparation

Tackle two sample interview problems in probability, step-by-step.

3. 3

### Expected Value

Strategies to calculate the average outcome of random variables.

1. Included with

#### Expected Value

Trading is often a game of averages. Learn how to quantify them.

2. Included with

#### Expected Utility

When risk is involved, expected values get more complex!

3. Included with

#### Interview Preparation

Tackle a sample interview problem in expected value, step-by-step.

4. 4

### Variance

The real way to measure "a crazy day on Wall Street".

1. Included with

#### Variance

Learn essential techniques for modeling the fluctuations of assets and quantifying risk.

2. Included with

#### Covariance

Assets are often correlated. Get to know this tool for measuring how their relative fluctuations affect others.

3. Included with

#### Indicator Variables

Learn a trick for calculating variance that works even when events are dependent.

4. Included with

#### Interview Preparation

Tackle a sample interview problem in variance, step-by-step.

5. 5

### Statistics

Your model looks good, but are the results statistically significant?

1. Included with

#### Statistics

Statistics gathers information from samples to make inferences about the overall population.

2. Included with

#### Normal Distributions

Though it's not a perfect model, this distribution remains at the core of many pricing algorithms.

3. Included with

#### Log-normal Distributions

Get familiar with one of the most common distributions used to model asset prices.

6. 6

### Confidence and Estimation

Learn how to estimate and how confident you should be.

1. Included with

#### Hypothesis Testing

Hypothesis testing helps determine if your model is actually consistent with the real-world data.

2. Included with

#### Parameter Estimation

Given some 'true' model, what are the parameters for that model that fit the data?

3. Included with

#### Fermi Estimates

Learn how to quickly estimate values which would require extensive analysis to determine exactly.

7. 7

### Matrices

The arithmetic of linear algebra for regression, Markov chains, and more.

1. Included with

#### Operations

Brush up on matrix operations: addition, multiplication, transpose, and trace.

2. Included with

#### Inverses

Matrix inversion is an important tool to have on your belt when you're solving matrix equations.

3. Included with

#### Linear Systems

For large, real-world systems, this matrix approach is more effective than other ad-hoc techniques.

4. Included with

#### Covariance

Learn how to represent vector relationships, such as how stocks interact with each other.

8. 8

### Markov Chains

Stochastic modeling for the ever-changing markets.

1. Included with

#### An Overview of Markov Chains

Explore a powerful tool for representing systems that change states over time.

2. Included with