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Foundations of Probability

  • What is Probability?
  • Theoretical vs Empirical Probability
  • Three Views of Probability
  • Sample Space and Events
  • Axioms of Probability
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  • Variance and Standard Deviation
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Set Theory & Combinatorics

  • Set Operations in Probability
  • Counting Methods
  • Advanced Counting

Conditional & Bayesian Probability

  • Conditional Probability
  • Bayes' Theorem
  • Law of Total Probability

Random Variables & Distributions

  • What is a Random Variable?
  • Discrete vs Continuous
  • PDFs and CDFs
  • Expectation, Variance, and Moments

Discrete Distributions

  • Bernoulli and Binomial
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  • Negative Binomial and Hypergeometric

Continuous Distributions

  • Uniform and Normal
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  • Heavy-Tailed Distributions

Limit Theorems

  • Law of Large Numbers
  • Central Limit Theorem
  • Convergence in Probability vs Distribution

Frequentist Inference

  • Confidence Intervals
  • Hypothesis Testing
  • p-values and Statistical Decisions
  • Type I and Type II Errors
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Advanced Probability Tools

  • Law of the Unconscious Statistician
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Bayesian Inference

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Regression Analysis

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Multivariate Statistics

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Stochastic Processes

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Simulation & Approximation

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Time Series

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Information Theory

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Linear Algebra

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Calculus & Optimization

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Machine Learning Fundamentals

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Deep Learning

  • Feedforward Networks
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Options Pricing

  • Payoffs and Put–Call Parity
  • Risk-Neutral Valuation
  • Binomial Trees
  • Black–Scholes
  • The Greeks
  • Volatility Smile and Surface
  • Exotic Options

Portfolio Theory

  • Mean–Variance Optimization
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  • Sharpe, Sortino, and Information Ratio
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Trading & Risk Applications

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Study Guide/Options Pricing
Section 21 · Lesson 21.98

Risk-Neutral Valuation

Pricing derivatives by changing measure — and why it works.

Under the real-world ("physical") probability measure, asset prices have drift related to investors' expected returns. The risk-neutral measure (Q\mathbb{Q}Q) is a different probability measure under which all assets earn the risk-free rate.

The fundamental theorem of asset pricing: in the absence of arbitrage, there exists a risk-neutral measure such that every traded asset's discounted price is a Q\mathbb{Q}Q-martingale. Derivative prices are then expectations under Q\mathbb{Q}Q:

V0=e−rTEQ[payoffT]V_0 = e^{-rT} E^{\mathbb{Q}}[\text{payoff}_T]V0​=e−rTEQ[payoffT​]

The trick is that we don't need to know investors' actual risk preferences to price derivatives — those preferences are baked into the underlying's price already. Risk-neutral valuation lets us price by replication: simulate paths under Q\mathbb{Q}Q, compute expected discounted payoff, that's your fair price.

Practical implication: implied volatility is calibrated to the prices of liquid options, then used to price illiquid options consistently — without ever asking what real-world drift the underlying actually has.

Why can we price options using risk-neutral expectations rather than real-world expectations?

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Payoffs and Put–Call Parity
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Binomial Trees