Panos K. Pouliasis

Affine‐Structure Models and the Pricing of Energy Commodity Derivatives

We consider a seasonal mean-reverting model for energy commodity prices with jumps and Heston-type stochastic volatility, and three nested models for comparison. By exploiting the affine form of the log-spot models, we develop a general valuation framework for futures and discrete arithmetic Asian options. We investigate five major petroleum commodities from Europe (Brent crude oil, gasoil) and US (light sweet crude oil, gasoline, heating oil) and analyse the effects of the competing fitted spot models in futures pricing, Asian options pricing and hedging. We find evidence that price jumps and stochastic volatility are important features of the petroleum price dynamics.

Jumps and Stochastic Volatility in Crude Oil Prices and Advances in Average Option Pricing

Crude oil derivatives form an important part of the global derivatives market. In this paper, we focus on Asian options which are favoured by risk managers being effective and cost-saving hedging instruments. The paper has both empirical and theoretical contributions: we conduct an empirical analysis of the crude oil price dynamics and develop an accurate pricing set-up for arithmetic Asian options with discrete and continuous monitoring featuring stochastic volatility and discontinuous underlying asset price movements. Our theoretical contribution is applicable to various commodities exhibiting similar stylized properties. We here estimate the stochastic volatility model with price jumps as well as the nested model with omitted jumps to NYMEX WTI futures vanilla options. We find that price jumps and stochastic volatility are necessary to fit options. Despite the averaging effect, we show that Asian options remain sensitive to jump risk and that ignoring the discontinuities can lead to substantial mispricings.

Investor Sentiment, Earnings Growth, and Volatility

Abstract Using shipping sentiment proxies that capture market expectations, valuation, and liquidity, we investigate the role of investor sentiment in the dry bulk sector. This chapter highlights the importance of incorporating the time evolution of market optimism–pessimism into the commercial, operational, and financial decisions relating to shipping assets. We find that sentiment is a contrarian indicator of future earnings and report evidence of cross-sector sentiment contagion. We further infer that total sentiment has a positive impact on earnings’ volatility with high optimism (pessimism) leading to upward (downward) revisions in volatility. Fluctuations in earnings are driven by shocks to the sentiment level and volatility, while there is a two-way feedback relationship between sentiment and vessel earnings. Finally, as investor sentiment provides significant incremental information for predicting earnings and the corresponding volatility, its use can bestow benefits to participants establishing investment strategies and may have policy implications, in terms of stabilizing uncertainty.