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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
  • Independence and Expectation
  • Variance and Standard Deviation
  • Covariance and Correlation
  • Key Inequalities

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
  • Poisson and Geometric
  • Negative Binomial and Hypergeometric

Continuous Distributions

  • Uniform and Normal
  • Exponential, Gamma, Beta
  • 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
  • Power and Effect Size
  • Bootstrapping and Resampling

Advanced Probability Tools

  • Law of the Unconscious Statistician
  • Moment Generating Functions
  • Characteristic Functions
  • Markov Chains
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Bayesian Inference

  • Bayesian Philosophy
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Regression Analysis

  • Ordinary Least Squares
  • Multiple Linear Regression
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  • Regularization
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Multivariate Statistics

  • Joint, Marginal, and Conditional
  • Multivariate Normal
  • Covariance Matrices
  • Correlation vs Causation
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Stochastic Processes

  • Random Walks
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  • Brownian Motion
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Simulation & Approximation

  • Monte Carlo Simulation
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  • Bootstrapping for Finance
  • Quasi-Monte Carlo

Time Series

  • Stationarity and Autocorrelation
  • AR, MA, and ARIMA
  • GARCH and Volatility Clustering
  • Cointegration and Pairs Trading
  • Kalman Filters

Information Theory

  • Shannon Entropy
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Linear Algebra

  • Vectors, Norms, and Inner Products
  • Matrix Operations
  • Eigenvalues and Eigenvectors
  • Singular Value Decomposition
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Calculus & Optimization

  • Multivariate Calculus
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  • Gradient Descent and Variants
  • Stochastic Calculus Primer

Machine Learning Fundamentals

  • Supervised vs Unsupervised
  • Bias–Variance Trade-off
  • Cross-Validation
  • Tree-Based Methods
  • Support Vector Machines
  • Clustering and Dimensionality Reduction
  • Classification Metrics

Deep Learning

  • Feedforward Networks
  • Backpropagation
  • Optimizers and Schedules
  • Regularization in DL
  • Architectures for Finance
  • Loss Functions

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
  • CAPM and Factor Models
  • Sharpe, Sortino, and Information Ratio
  • Black–Litterman
  • Risk Parity

Trading & Risk Applications

  • Value-at-Risk
  • Expected Shortfall
  • Backtesting
  • Market Making Basics
  • Execution and Market Microstructure
  • Statistical Arbitrage
Study Guide/Options Pricing
Section 21 · Lesson 21.97

Payoffs and Put–Call Parity

What options pay, and the no-arbitrage relationship that ties calls and puts.

A European call gives the holder the right to buy at strike KKK at expiry TTT, paying max⁡(ST−K,0)\max(S_T - K, 0)max(ST​−K,0). A European put gives the right to sell, paying max⁡(K−ST,0)\max(K - S_T, 0)max(K−ST​,0).

Put-call parity ties them together. For a stock with no dividends:

C−P=S−Ke−rTC - P = S - K e^{-rT}C−P=S−Ke−rT

The argument is no-arbitrage. A long call plus short put pays ST−KS_T - KST​−K at expiry; a long stock plus short bond paying KKK at expiry pays ST−KS_T - KST​−K too. They must cost the same now, or you can arbitrage.

Put-call parity is one of the few model-free results in derivative pricing. It always holds (for European options on non-dividend-paying stock with constant rate), regardless of any model assumption about the dynamics of SSS.

If S=100S = 100S=100, K=100K = 100K=100, r=5%r = 5\%r=5%, T=1T = 1T=1 year, and the call is worth C=10C = 10C=10, what is the put worth (to no-arbitrage)?

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