Volatility risk is an important consideration when trading and investing in energy markets. To better understand the nuances around volatility in energy trading, there are some classic academic papers and reference notes that provide useful insights. These volatility references cover topics like pricing models, statistical analysis, and risk management strategies. By reviewing the key conclusions from seminal works by academics like Avellaneda, Schwartz, Manoliu and others, traders can enhance their knowledge of concepts like mean reversion, stochastic volatility, and managing volatility spikes. Gaining expertise in theoretical foundations as well as practical considerations around volatility will lead to better outcomes when deploying energy trading strategies.
Mean Reversion and Stochastic Volatility Are Key Properties of Commodities Like Energy
The paper ‘Mean Reversion and Stochastic Volatility of Commodity Prices’ by Schwartz and Smith provides evidence that commodities like crude oil exhibit mean reversion and stochastic volatility in their price behavior. Using detailed statistical analysis on futures prices, they demonstrate empirically that commodities follow an Ornstein-Uhlenbeck process with a stochastic variance rate. These properties have implications for valuation and risk management. Traders should be aware of the tendency for commodity prices to revert to a long-term equilibrium level. However, the volatility is changing over time, which contributes to uncertainty in forecasting. The quantitative findings highlight that naive models assuming constant volatility understate the true volatility in energy markets.
Pricing Models Need To Capture Volatility Skew and Kurtosis
In the paper ‘Pricing Commodity Derivatives with Basis Risk’, Manoliu develops an advanced option pricing model tailored for commodities. A key contribution is incorporating stochastic volatility and jumps, which leads to more accurate modeling of the volatility skew and leptokurtosis observed in commodity returns. The paper demonstrates how to calibrate the model parameters based on market data. Manoliu concludes that standard pricing models like Black-Scholes underestimate the true volatility, while this advanced model provides superior pricing and hedging performance. Energy traders should be aware of shortcomings in basic models, and consider advanced alternatives that better capture the unique risk profiles of commodities.
Risk Management Strategies for Volatility Spikes in Energy Trading
The book chapter ‘Managing Energy Price Risk’ by Avellaneda provides an overview of risk management strategies for energy trading. One of the key risks highlighted is volatility spikes, which can lead to large losses if not controlled. Avellaneda discusses techniques like value-at-risk, stop-loss orders, options hedging, and diversification as ways to mitigate volatility risk. For example, a long condor options position has limited downside but allows profits from volatility expansion. Since volatility has stochastic behavior in energy markets, combining static and dynamic hedging approaches is advised. Energy traders should continually evaluate their portfolio risks, and adjust hedging positions to reflect changes in volatility regimes.
Classic references on volatility in energy trading emphasize key properties like mean reversion and stochastic variance rates. Advanced option pricing models better capture the volatility skew and leptokurtosis seen in energy markets. Risk management strategies are needed to protect against volatility spikes which can quickly lead to losses.