> ## Content Index > Fetch the complete content index at: https://fsc.stevens.edu/llms.txt > Use this file to discover other available public pages before exploring further. # Optimizing Risk and Return: Statistical & Volatility Modeling of Trading Strategies - URL: https://fsc.stevens.edu/optimizing-risk-and-return-statistical-volatility-modeling-of-trading-strategies/ - Published: 2026-04-06T21:03:28.000Z - Updated: 2026-05-16T22:33:29.000Z - Author: Peterson Iyayi Inneh - Tags: 2025 Spring, FE 800 Projects Spring 2025 ## Researcher Vaishnavi Sampath ## Faculty Advisor Professor Zhenyu Cui ## Overview This project develops and evaluates trading strategies for major energy-sector stocks by combining **statistical forecasting, volatility modeling, macroeconomic analysis, and machine learning**. The main objective is to identify strategies that can improve returns while controlling risk, with special attention to how **volatility influences performance**. The analysis focuses on **Exxon, Chevron, Shell, British Petroleum, and National Fuel Gas**, using benchmark data from **WTI, Brent crude, Henry Hub natural gas, the S&P 500, and macroeconomic indicators**. The project applies models such as **ARIMA, GARCH, SVAR, CAPM, logistic regression, KNN, and Random Forest**, then compares multiple trading strategies including **trend following, pairs trading, momentum, and mean reversion**. The overall goal is to determine whether these models and strategies can outperform traditional approaches in terms of **risk-adjusted returns, forecasting reliability, and practical trading performance**. ## Key Findings ### 1\. Market and Model Validation. - The statistical analysis confirmed that the stock and benchmark return series were generally **stationary**, making them suitable for time-series modeling. - **ARIMA models** were able to capture short-term return behavior for several assets, though many price forecasts remained relatively flat, showing the limits of traditional forecasting in directional prediction. - **GARCH models** were effective in identifying **volatility clustering**, especially for major oil-linked equities and benchmarks. - The analysis showed that oil-related stocks were strongly influenced by broader energy market movements, with stocks like **Exxon and Chevron reacting closely to WTI**, and **Shell and BP reacting more to Brent**. ### 2\. Impact of Volatility and Energy Shocks - Volatility played a major role in determining the effectiveness of both forecasting models and trading strategies. - The **SVAR analysis** showed that shocks in **WTI, Brent, and Henry Hub** affect macroeconomic indicators, industrial production, price indices, and carbon-related variables, though many of these effects were **short-lived or moderate**. - **WTI and Brent** had stronger and more persistent spillover effects across economic and industrial variables than **Henry Hub**, which tended to have more localized influence. - Energy shocks were shown to matter not only for stock returns, but also for **production, inflation-related indices, and sector-level forecast uncertainty**. ### 3\. Trading Strategy Methodology and Performance - **SMA-based Trend Following** performed best on **Exxon and National Fuel Gas**, where stronger persistent trends allowed the strategy to generate better cumulative returns and Sharpe ratios. - **Pairs Trading** between **Exxon/Chevron** and **Shell/BP** produced moderate results, but the lack of statistically strong cointegration weakened the long-term mean-reversion assumption behind these trades. - The **KNN-based Momentum Strategy** performed poorly overall, with low win rates, frequent false signals, and weak Sharpe ratios across most stocks. - The **Random Forest Mean Reversion Strategy** produced mixed outcomes, with **Exxon and Shell** showing the best relative performance, while **Chevron and BP** struggled with weak risk-adjusted returns and larger drawdowns. ### 4\. Comparative Insights - Traditional statistical models such as **ARIMA and GARCH** were useful for understanding return structure and volatility, but they had limited ability to produce strong directional forecasts on their own. - **Rule-based strategies** like SMA trend following were more successful when the underlying asset exhibited clear trends and manageable volatility. - **Machine learning models** did not consistently outperform simpler methods; in some cases, they generated excessive trades, low win rates, and unstable performance. - Across the project, the most important lesson was that **risk control, volatility awareness, and model-asset fit** matter more than model complexity alone. ## Conclusion This project shows that profitable trading is not simply about using advanced models, but about selecting strategies that align with the **behavior, volatility profile, and structural characteristics** of each asset. Statistical models provided a strong analytical foundation, while volatility modeling helped explain why some strategies worked under certain market regimes and failed under others. The findings suggest that **trend-following approaches** can work well in persistent markets, while **mean-reversion and machine learning strategies** require stronger assumptions, better feature design, and more robust validation to be reliable in practice. Future research should: - incorporate **transaction costs, slippage, and real-world trading frictions** - use **dynamic signal thresholds and stronger risk management tools** - include **additional macroeconomic and fundamental variables** - apply **walk-forward optimization** more extensively - explore **news, sentiment, and NLP-based event signals** - further refine machine learning models to better adapt to changing market regimes ## Significance This project is significant because it connects **quantitative finance, volatility modeling, macroeconomic analysis, and algorithmic trading** in one integrated framework. It demonstrates that strong trading performance depends not just on prediction accuracy, but on how well models respond to risk, changing market conditions, and asset-specific dynamics. By comparing classical statistical models, economic structure models, and machine learning strategies, the project provides a practical view of what works, what fails, and why. It offers useful insight for designing more robust trading systems that aim to improve **risk-adjusted returns rather than raw returns alone**.