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ANALYSIS & MODELING

Quant Methods

Systematic investing runs on statistics, and this category covers the toolkit.

The explainers work through volatility modeling with GARCH and stochastic volatility, regime-switching and hidden Markov models, copulas, extreme value theory, and cointegration for pairs and relative value, then the Kalman filter, the Hurst exponent, and price-impact measures like Kyle's lambda.

Every technique is tied to a job: what it estimates, when it helps, and where it quietly breaks down.

These methods sit under modern risk systems and signal research alike.

Investing With Purpose keeps the math purposeful rather than ornamental, so you finish understanding the models that drive the quantitative side of markets, not just their names.

Quant Methods
Amihud Illiquidity Measure: Price Impact from Daily Volume

The Amihud illiquidity measure, known as ILLIQ, estimates how much a stock's price moves per dollar of trading. A…

Intermediate
Quant Methods
Transaction Cost Analysis: Measuring Hidden Execution Costs

Transaction Cost Analysis (TCA) measures the real cost of executing trades and compares it to benchmarks. Good TCA…

Intermediate
Quant Methods
Prime Brokerage Mechanics: Financing, Custody, and Short Borrow

A **prime broker** is a large bank or broker-dealer that sits at the center of a hedge fund's operations. It provides…

Intermediate
Quant Methods
GARCH Volatility Modeling: Forecasting Time-Varying Market Risk

GARCH is a time-series model that treats a market's variance as a process with memory. It captures the empirical fact…

Advanced
Quant Methods
Stochastic Volatility Model: How Options Smile Gets Priced

Stochastic volatility models treat volatility itself as a random process driven by its own source of randomness, not as…

Advanced
Quant Methods
Regime-Switching Model: Detecting Hidden Market States

Regime-switching models assume that the parameters governing a time series change discretely between a small number of…

Advanced
Quant Methods
Copulas in Finance: Modeling Joint Tail Dependence

A copula is a mathematical function that couples the marginal distributions of several random variables into a joint…

Advanced
Quant Methods
Extreme Value Theory Finance: Estimating Rare Loss Quantiles

Extreme Value Theory is the branch of statistics that deals with the tails of distributions rather than their middles.…

Advanced
Quant Methods
Cointegration Engle Granger Johansen: Pairs Trading Foundation

Cointegration is the property that two or more non-stationary time series can share a long-run equilibrium even though…

Advanced
Quant Methods
Kalman Filter Finance: Tracking Time-Varying Hedge Ratios

The Kalman filter is a recursive algorithm that estimates the hidden state of a linear dynamic system from a stream of…

Advanced
Quant Methods
Hidden Markov Model Finance: Inferring Market Regimes

A Hidden Markov Model (HMM) is a probabilistic framework for time series where an unobserved state follows a Markov…

Advanced
Quant Methods
Hurst Exponent: Detecting Trend vs Mean-Reversion

The Hurst exponent is a single number that summarizes the long-range dependence of a time series. It classifies a…

Advanced
Quant Methods
Kyle's Lambda: Measuring Order-Flow Price Impact

Kyle's lambda is the slope that links signed order flow to price changes. A higher lambda means each unit of buying or…

Advanced
Quant Methods
Market Impact Model: Estimating Trade Cost Before Execution

A market impact model predicts how much a trade will move the price against itself. Two canonical frameworks dominate:…

Advanced
Quant Methods
Execution Algorithms VWAP TWAP: Choosing the Right Approach

Execution algorithms break a parent order into many child orders and send them to the market according to a rule. The…

Advanced
Quant Methods
Implementation Shortfall: The Honest Execution Scorecard

Implementation shortfall (IS) measures the gap between the return of a paper portfolio, executed at the price when the…

Advanced
Quant Methods
Slippage Modeling: Predicting Execution Cost Before You Trade

Slippage is the difference between the price you expected and the price you got. A slippage model predicts that…

Advanced
Quant Methods
Volatility Clustering: Why Calm Follows Calm, Chaos Follows Chaos

Volatility clustering is the empirical observation that large price moves tend to be followed by more large moves, and…

Advanced
Quant Methods
Fat Tails Kurtosis: Why Crashes Happen More Than Models Predict

Fat tails describe a return distribution where extreme outcomes occur far more often than a normal distribution would…

Advanced
Quant Methods
Skewness in Returns: Why Crashes Outpace Rallies

Skewness measures how asymmetric a return distribution is. Negative skew means the left tail is longer or heavier than…

Advanced
Quant Methods
Serial Correlation Returns: When Past Returns Predict Future Returns

Serial correlation, also called autocorrelation, measures how today's return relates to returns from previous periods.…

Advanced
Quant Methods
Maximum Likelihood Estimation Finance: Fitting Models to Data

Maximum likelihood estimation is a general method for fitting a statistical model by choosing parameters that make the…

Advanced
Quant Methods
Bayesian Inference Trading: Shrinking Noisy Alpha Estimates

Bayesian inference combines a prior belief about unknown quantities with observed data to produce an updated, posterior…

Advanced
Quant Methods
Ensemble Methods Finance: Combining Models to Reduce Forecast Error

Ensemble methods combine predictions from many models into a single, more reliable output. In finance, they are the…

Advanced
Quant Methods
Feature Engineering Finance: Building Inputs That Predict Returns

Feature engineering is the process of turning raw market, fundamental, and alternative data into inputs that a…

Advanced
Quant Methods
VWAP TWAP Execution Algorithm: Schedule-Based Institutional Trading

VWAP and TWAP are two of the oldest scheduled execution algorithms used by institutional traders to break a large order…

Advanced
Quant Methods
Implementation Shortfall Algorithm: Protecting the Decision Price

An implementation shortfall (IS) algorithm tries to minimize the gap between the price at the moment the decision was…

Advanced
Quant Methods
POV Participation Algorithm: Trading as a Fraction of Market Flow

A Percent of Volume (POV) algorithm, also called a participation algorithm, executes a parent order as a fixed fraction…

Advanced
Quant Methods
Sniper Liquidity Seeking Algorithm: Hunting Hidden Block Liquidity

Sniper and liquidity-seeking algorithms are opportunistic execution engines that hunt displayed and hidden size across…

Advanced
Quant Methods
Dark Pool Routing: Scoring Off-Exchange Venues for Best Fills

Dark pool routing is the set of rules an execution system uses to decide which off-exchange venues to send child orders…

Advanced
Quant Methods
Smart Order Router: Sweeping All Venues for Best Execution

A smart order router (SOR) is the execution-layer engine that decides which venues to send each child order to, in…

Advanced
Quant Methods
Transaction Cost Analysis TCA: Proving Best Execution Quarter by Quarter

Transaction cost analysis is the measurement discipline that compares the price a trader actually paid against a set of…

Advanced
Quant Methods
Almgren-Chriss Optimal Execution: The Math Behind IS Algorithms

The Almgren-Chriss model is the benchmark mathematical framework for optimal trade execution. Published in 2000 in the…

Advanced