Newsletter: PCA in Action, From Commodity Derivatives to Dispersion Trading

How PCA is used in pricing commodity derivatives and dispersion trading

Principal Component Analysis (PCA) is a dimensionality reduction technique used to simplify complex datasets. It transforms the original variables into a smaller set of uncorrelated variables called principal components, ranked in order of their contribution to the dataset's total variance. In this issue, we’ll discuss various applications of PCA.

Web-only posts Recap

Below is a summary of the web-only posts I published during last week

-Formal Study of Overfitting in Trading System Design

-How Algorithmic Trading Impacts the Markets

-Momentum in the Option Market, Intraday Case

-Using Machine Learning to Identify Drivers of Oil Price

-Momentum in the Option Market

-Selecting Pairs Using Principal Component Analysis

Recent Newsletters

Below is a summary of the weekly newsletters I sent out recently

-CAPM, WACC, and Beyond: Beta’s Application in Arbitrage (8 min)

-Wrapping Up 2024-Crypto Trading Strategy and Money Laundering Threats (6 min)

-The Role of Gold in Your Portfolio, Insights and Entertainment (7 min)

-Option Pricing Models and Strategies for Crude Oil Markets (8 min)

Pricing Commodity Derivatives Using Principal Component Analysis

Due to the seasonal nature of commodities, pricing models should be able to take into account seasonality and other deterministic factors.

Reference [1] proposed a new, multi-factor pricing method based on Principal Component Analysis (PCA). It introduces a multi-factor model designed to price commodity derivatives, with a particular focus on commodity swaptions.

Findings

- The model calibration process consists of two key steps: offline and online.

- The offline step, conducted infrequently, determines mean reversion rates, the ratio of long and short factor volatilities, and the correlation between the factors using historical data.

- The online step occurs every time the model is used to price an option or simulate price paths.

- Empirical analysis demonstrates that the model is highly accurate in its predictions and applications.

- Swaptions, which are relatively illiquid commodities, present a challenge due to their one-sided natural flow in the market.

- Model calibration strategies are divided into seasonal and non-seasonal categories, considering the asset's characteristics. For seasonal assets like power or gas, local volatilities are calibrated separately for each contract, while a boot-strapping strategy is employed for non-seasonal assets like oil.

- Currently, the multi-factor model lacks a term structure for volatility ratios and mean reversions. However, it can be easily extended to incorporate a time dependency, which would facilitate fitting market prices of swaptions across various tenors.

Reference

[1] Tim Xiao, Pricing Commodity Derivatives Based on A Factor Model, Philarchive

Dispersion Trading Using Principal Component Analysis

Dispersion trading involves taking positions on the difference in volatility between an index and its constituent stocks.

Reference [2] examined dispersion trading strategies based on a statistical index subsetting procedure and applied it to the S&P 500 constituents

Findings

- This paper introduces a dispersion trading strategy using a statistical index subsetting approach applied to S&P 500 constituents from January 2000 to December 2017.

- The selection process employs principal component analysis (PCA) to determine each stock's explanatory power within the index and assigns appropriate subset weights.

- In the out-of-sample trading phase, both hedged and unhedged strategies are implemented using the most suitable stocks.

- The strategy delivers significant annualized returns of 14.52% (hedged) and 26.51% (unhedged) after transaction costs, with Sharpe ratios of 0.40 and 0.34, respectively.

- Performance remains robust across different market conditions and outperforms naive subsetting schemes and a buy-and-hold approach in terms of risk-return characteristics.

- A deeper analysis highlights a correlation between the chosen number of principal components and the behavior of the S&P 500 index.

- An index subsetting procedure was developed, considering the explanatory power of individual stocks, allowing a replicating option basket with as few as five securities.

- An analysis of sector exposure, principal components, and robustness checks demonstrated that the trading systems have superior risk-return characteristics compared to other dispersion strategies.

Reference

[2] L. Schneider, and J. Stübinger, Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns, Mathematics 2020, 8, 1627

Closing Thoughts

PCA is a powerful tool in quantitative finance. In this issue, we have demonstrated its effectiveness in pricing commodity derivatives and developing dispersion trading strategies. Its versatility extends beyond these applications, making it a valuable technique for tackling a wide range of problems in quantitative finance.

Educational Video

Advanced Pairs Trading: The Principal Component Analysis (PCA) Approach

In this video, IllyaBarziy, Quant Research Team Lead at Hudson and Thames, goes over the approach proposed in the paper "Statistical arbitrage in the US equities market"(2010) by Avellaneda, M. and Lee, J.-H. This strategy produced an annualized Sharpe ratio of 1.44 over the period from 1997 to 2007.

The idea is to decompose stocks returns into a systematic component and an idiosyncratic component (residual). In order to get the systematic component, the eigen portfolios from the PCA Approach are used.

The portfolio representing the idiosyncratic component can then be traded based on its deviation from equilibrium values.

Around the Quantosphere

- It's Tough to Be a Research Analyst (Bloomberg, paywall)

- Quant trader accused of stealing code (investmentexecutive)

- Veteran trader plans new hedge fund to capitalise on market volatility (hedgeweek)

- Hong Kong Working Harder to Attract Family Offices and More Asia Real Estate Headlines (mingtiandi)

Disclaimer

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