Global commodity futures market modelling and statistical inference

Tang, Weiqing (2018). Global commodity futures market modelling and statistical inference. University of Birmingham. Ph.D.

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Abstract

This thesis first investigates the asset pricing ability of a new risk factor, namely Risk-Neutral Skewness (estimated based on option data) in the global commodity futures market. Skewness trading behaviour in the option market is attributed to heterogeneous belief and selective hedging concern. The negative (positive) the Risk-Neutral Skewness is accompanied with excess trading on put (call) option contracts, which leads to underlings' over-pricing (under-pricing). Above results are robust to time-series and cross-sectional test and other alternatives.

Secondly, a new functional mean change detection procedure is proposed via the Kolmogorov-Smirnov functional form. Simulations indicate decent testing power under the alternative. An empirical test procedure is deployed for crude oil and gold futures price term structure, showing real market data change. The multivariate forecasting regression analysis uncovers trading behaviours behind the real-world change occurrence.

Lastly, the futures basis term structure is forecasted under the framework of the functional autoregressive predictive factor model with lag 1. By comparison, the new method outperforms other functional and non-functional methods, with maturities less than 10 months. The Model Confidence Set method statistically validate this result. A new variance minimization trading strategy is proposed and tested when the future futures basis is forecast and known.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):