School of Quants
Курс "Школа квантов" (School of Quants) появился по инициативе команды "Школы финансов ЭФ МГУ", ведущей дискуссионной площадки по финансам.
Годовой цикл School of Quants состоит из еженедельных мероприятий специалистов финансовой сферы, включает обсуждение теоретических и практических аспектов тем по анализу и моделированию стоимости финансовых продуктов, статистическому и эконометрическому анализу, финансовому программированию, машинному обучению, построению торговых стратегий, риск-менеджменту, а также командный и индивидуальный ситуационный разбор кейсов.
Структура курса:
1. Стохастические методы (Stochastic Calculus)
- Brownian Motion specification and construction, correlated Brownian Motions, features of the Brownian Motion path
- Martingales and their properties, conditional expectations, elements of martingale analysis
- Ito stochastic integral, Ito calculus, Ito’s lemma
- Stochastic differential equations, arithmetic Brownian motion, geometric Brownian Motion, mean reverting
- Options Pricing Models: Binomial Model, Black-Scholes Model
2. Финансовые продукты и рынки (Products & Markets)
- Equities and Equity DerivativesFX and FX Derivatives
- Commodity and Commodity Derivatives
- Models and methods for Valuation of Vanilla Products
- Models and methods for Valuation of Exotic and Path-Dependent products
- Fundamentals of Equities, Commodities and FX Volatility Modelling
- Rates Markets, Swaps and Derivatives
- Yield Curve EstimationInterest Rate Models
- Interest Rate Volatility Modelling
- Valuation of Vanilla Products and Exotic Interest Rate Derivatives
- Credit Markets, Vanilla Instruments (CDS/CDX), CDX Options and Correlation Trading
- Structural and Reduced Form credit models
3. Количественные стратегии в финансах (Trading Strategies)
- Option Greeks and Delta Hedging: Characteristics & Greeks based trading strategies
- Implied volatility, smile, skew and forward volatility
- Different types of Momentum (Time series & Cross-sectional)
- Trend following strategies and Statistical Arbitrage Trading strategy modeling with Python
- Arbitrage, market making and asset allocation strategies using ETFs
- Non-linear strategies
- Code and back-test different strategies on various platforms
4. Статистический анализ в финансах (Advanced Statistics)
- Data Visualization: Statistics and probability concepts (Bayesian and Frequentist methodologies), moments of data and Central Limit Theorem
- Applications of statistics: Random Walk Model for predicting future stock prices using simulations and inferring outcomes, Capital Asset Pricing Model
- Modern Portfolio Theory - statistical approximations of risk/reward
- Linear regression
- Time series analysis and statistical functions including autocorrelation function, partial autocorrelation function
- Maximum likelihood estimation, Akaike Information Criterion
- Stationarity of time series, Autoregressive Process, Forecasting using ARIMA
- Difference between ARCH and GARCH and Understanding volatility
- Ridge Regression and Lasso Regression for prediction optimization
5. Финансовое программирование в Python (Financial Programming in Python)
- Data types, variables, Python in-built data structures, inbuilt functions, logical operators, and control structures
- Python concepts for writing functions - functional programming
- Implement various OOP concepts in Python program - Aggregation, Inheritance, Composition, Encapsulation, and Polymorphism
- Introduction to some key libraries NumPy, pandas, and matplotlib
- Back-testing methodologies & techniques and using Random Walk Hypothesis
- Work on sample strategies, trade the Boring Consumer Stocks in Python
6. Финансовое программирование в C++ (Financial Programming in C++)
- Basic C/C++ Language and Syntax
- OOP in C++
- Inheritance and Polymorphism
- Generic Programming in C++ and Standard Template Library (STL)
- An Introduction to Boost C++ Libraries
- Applications in Computational Finance
7. Машинное обучение в финансах (Machine Learning in Finance)
- Modeling data with AI, index and predicting next day’s closing price
- Supervised learning algorithms, Decision Trees & additive modeling
- Intro to neural networks
- Natural Language Processing (NLP) and Sentiment Analysis
- Confusion Matrix framework for monitoring algorithm’s performance
- Logistic Regression to predict the conditional probability of the market direction. Cross validation
- Understand principle component analysis and back-test PCA based long/short portfolios
- Reinforcement Learning in Trading
- Clustering algorithms
8. Риск-менеджмент и оптимизация портфеля (Risk Management and Portfolio Optimization)
- Different methodologies of evaluating portfolio & strategy performance
- Risk Management: Sources of risk, risk limits, risk evaluation & mitigation, risk control systems
- Basic Risk Management Technics: Parametric Linear VaR Models, Historical Simulation and MonteCarlo Methods, Scenario Analysis and Stress Testing, xVA
- Trade sizing for individual trading strategy using conventional methodologies, Kelly criterion, Leverage space theorem
Руководитель и координатор курса: Ахмедзянов Руслан Германович
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