• Conducted a volatility study to develop pairs trading strategy by writing web crawlers that automated extracting 30 equity and ETF spot and options prices data from CBOE and Yahoo Finance • Utilized NumPy, Pandas, and SciPy packages to calculate implied volatility, realized volatility, and risk premiums to measure how the market prices risk • Gathered and plotted daily VIX futures data with Selenium, Seaborn and Matplotlib to study volatility term structure • Examined volatility clustering and built forecasting tools for market risk using correlations of daily absolute returns and volatility at different time lags

Fama-French models, idiosyncratic volatility, event study

This study develops a non-invasive electronic nose system for real-time infection monitoring in neonatal incubators by identifying pathogens through microbial volatile organic compounds (mVOCs).

This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.

In the last month, Caio and I developed a Python script capable of deriving the Black & Scholes equation and finding all Greeks' equations, including Third-degree Greeks. In addition, we developed the graphic representation of these Greeks' behavior to variations in terms of time, implied volatility, dividends, risk-free rate, and stock price. Moreover, in this study, we found new important greeks that weren't cataloged before. As an example, we named one of them as ''Tau'' (It's demonstrated and analyzed at the end of this report)

Volatility Arbitrage study

Financial Big Data (FIN-525) final project: The Impact of COVID-19 on Returns and Volatility: a case study of the United States, China, Switzerland and Japan

This is the code notebook for the paper on using Python to study the volatility of BTC in ARIMA-EGARCH model.

This project compares the LSTM neural network and GARCH econometric models in predicting stock price volatility and estimating Value-at-Risk (VaR). The study evaluates these models' accuracy in forecasting Indonesian stock volatility using performance metrics like MAE, RMSE, and MAPE, aiming to guide investment risk assessment.

The goal of this study is to predict prices for Cryptocurrencies using Time series analysis and machine learning techniques. The purpose of this project is to take a sneak peek into the future by forecasting the next 30 days' average daily Realized Volatility (RV) of ETH-BTC using 2 different approaches - the traditional econometric approach to volatility prediction of financial time series GARCH and state-of-the-art LSTM Neural Networks. Quantitative research methodology was used in this study and the The dataset Consist the historical data values of any any crypto-pair such as Open/Close/High/Low prices of any interval such as 15-minutes, Hourly, 1-day interval weekly, monthly. Dataset were obtained using the Binance API .

A study of Markov Chain Monte Carlo and Stochastic Volatility Model

A study of implied volatility (iv) vs realized volatility (rv) of bitcoin options & attempt to use regression of rv to predit iv

Study on volatility transmission and protuberance among developed and developing stock markets using multivariate GARCH

In an era marked by unprecedented financial market volatility, this study introduces the integration of Hidden Markov Models with the Black-Litterman framework, proposing a novel method for portfolio optimization that skillfully navigates through the changing currents of economic indicators and market regimes.

This is a thesis topic which studies option pricing using (possibly deep) neural networks with outset in McGhee (2018) "An Artificial Neural Network Representation of the SABR Stochastic Volatility Model" and particularly focuses on implementation in Python.

This project focuses on examining the asymmetric volatility spillover effects between the Shanghai crude oil futures market and stock markets (both domestic and international) from 2018 to 2023. The study uses innovative spillover measures to capture time-varying asymmetry in the relationship between oil and stock markets.

This study proposes a new Forex trading method by combining Genetic Algorithms (GAs) and Directional Change (DC) strategies. The synergy improves trading results through better adaptation to market volatility.

The objective of this paper is to show the performance of volatility forecasting using different GARCH models. In particular, this paper attempt to capture the leverage effects in gold returns depending on regime changes which are strongly influenced by market conditions.

This project develops a quantitative portfolio optimization framework applied to a multi-asset universe. The study evaluates and compares several allocation strategies: Equal-Weight (EW) Minimum Variance (MinVol) Risk Parity (RP) Maximum Sharpe Ratio (MaxSharpe) Volatility Targeting (VolTarget)

Data-driven study of luxury market resilience (LVMH, Kering, Prada | Python (Pandas) + Tableau) by normalizing growth indices, calculating CAGR and volatility, and visualizing yearly returns, the project uncovers how these companies move in sync—or diverge—under different macroeconomic conditions.

In this study, I examine the effect of oil price shocks on four key US macroeconomic indicators -- real interest rates, industrial production, unemployment rate, and inflation. I use local projections to study how a one percent shock in the oil prices affects the US economy, which is essential, given the current volatility in the market for oil. I find that an increase in the oil prices has a strong and significant impact on all four indicators, implying that oil prices play an important role in the state of the US economy.

An interactive tool for studying recent changes in volatility regimes.

Python Statsmodel, Pandas, Seaborn on study on ESG vs stock price volatility.

Replicate the study 'Can LSTM outperform volatility-econometric models?' with SSE 50 etf

This project explores the influence of cryptocurrency volatility, particularly Bitcoin and Ethereum, on traditional stock indices like S&P 500, NASDAQ, and Dow Jones. Using advanced econometric models such as ARCH and GARCH

Short study on the effect of the various parameters in the Heston model (stochastic volatility) in option pricing.

This course is designed for analysts interested in pursuing a career in financial services with an emphasis on business analytics. Financial services traditionally includes banking, insurance, securities markets, and regulators. However, financial services are also found in organizational treasuries, departments of finance, CFO offices, investor relations, and any project where budgets, valuations, road maps, planning, and process efficiency require future projections of cash flows, growth rates, risk, and return. An introduction to methods and tools useful in decision-making in the financial industry, which may from time to time include: macroeconomic event studies, analysis of term structures, equity data analysis, style analysis, credit risk, extreme value analytics, trading analytics, volatility measurement, portfolio management, and enterprise risk management.

Textile_Price-Forecasting Cotton is a commodity, which is perhaps the most volatile among all the agricultural commodities traded.Due to high volatility in cotton prices, it is very difficult to predict the future market trend and accordingly sales strategy so as to compete in the market and increase the volume of the corporation for substantial growth .India is the largest cotton producing and second largest cotton exporting country. India accounting about 26% of the world cotton production. It has the distinction of having the largest area under cotton cultivation in the world with about 11-12 million hectares and constituting about 40% of the world area under cotton cultivation. Cotton is a global crop with high price fluctuation, which depends on the global business cycles. It is a mostly used as raw material for apparel and cloth industry. In addition to production risk cotton farmers encounter high price risk. Thus, it is important to forecast the cotton prices for the benefit of farmers as well as millers who purchase the cotton. The present study is aimed to forecast the prices of cotton of major producing states of India.Thus, Artificial intelligence based solution which may calculate the probability of future market trend and provide the forecasted price which can then be used to plan suitable pricing stategy for sale of cotton

I try learn a course that focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.

This project to examine the volatility characteristics of an equity. It used yahoo finance library to download a stock daily closing prices