Ivan Kolesnikov Email & Phone Number

Москва, Москва, Россия

Director at Quantitative Finance team, Data Analysis and Modelling department

Stack: QuantLib, Optuna, Scipy, PySpark- Developed and calibrated stochastic models for simulating yield curves and foreign exchange rates (GBM, Bates, HJM).- Refactored and enhanced the existing Python library: implemented abstract parent classes, refacrored and optimized code, wrote style guides.

Assistant lecturer for Quantitative Finance (in addition to previous roles)

Москва, Россия

- Class teacher for Quantitative Finance (2021-...)- Class teacher for Time Series and Panel Data Analysis (2023-...) - Coursework supervisor

Московская Область, Россия

Stack: QuantLib, GEKKO, Scipy, PyTorch, CatBoost, HyperOpt, StreamLit, gRPC, Kubernetes- Developed and launched fixed-income portfolio strategies for retail investors, outperforming index-based benchmarks.- Created highly customizable and robust fixed-income optimization library in Python.- Automated anf implemented the pipeline for trading and portfolio.

Московская Область, Россия

Московская Область, Россия

Stack: Statsmodels, Scipy, Scikit-Learn, MinIO (S3)-Consolidated the market risk validation and established it as a separate validation branch:- Increased the number of covered models from 2 to 30.- Validated the wide range of VaR and ALM models: analyzed and retrained them, selected relevant tests, prepared challenger models, wrote reports and.

Московская Область, Россия

Moscow, Russia

• Analyzed riskiness of the long-short strategies, including ADR and index arbitrages. Computed haircuts and required collateral sizes using statistical models and Monte-Carlo techniques.
• Created and maintained scripts, internal libraries, manuals. Automated a wide range of tasks including regulatory and internal reports, risk estimation, data collection, and interaction with trading systems.
• Developed and trained a classifier model to estimate the enhanced VaR of newly-brought securities for the REPO deals.

Boston, Massachusetts, United States

- Computed the risk-neutral moments (volatility, skewness, kurtosis) for S&P500 options for each day from 2005 to 2020. (Approx. 5500 prices per day, >20m in total)- Calculated a dynamic parameter that predicts a probability of a market crash within the next period. It successfully predicted the 2008 and 2020 crises two weeks before each.- Constructed and.

Moscow, Russia

Off-Cycle intern in the Fundamental Equity Research team

Moscow, Russia

Summer intern in the Project Finance (Internal Consulting) team

Hong Kong, Hong Kong SAR

Summer intern in the Sales and Marketing team

Master'S Degree, Financial Engineering

• Merit-based scholarship, covering 15% of tuition fees • Member of the MFE class council, Officer of Academics • GPA 3.85/4.0

Certificate Prorgram, Quantitative Finance

Bachelor Of Science - Bs, Economics And Finance

First Class Honours

Bachelor Of Science - Bs, Economics, Gpa 8.2/10.0 (12Th Out Of 190 Students)

Activities and Societies: • Organizer of the Philosophy Club. • Academic representative of the class.• Merit-based scholarship, covering.