Liquidity is one of the most intensively topics researched in financial economics for the last decade. Against this backdrop, this thesis attempts to address issue of liquidity in derivative markets and derivative models.
It begins with the provision of empirical evidence that liquidity risk can serve as an additional risk factor to market risk factor in pricing the commodity futures and it also outlines the vital role played by liquidity in futures prices of commodity co-movement and co-integration. Empirical evidence yields strong support on futures pricing model building, where factors should include both market risk and liquidity risk.
On above basis, this thesis builds two-factor futures pricing model by taking liquidity risk into account. I have also used 20-year oil futures market data to empirically justify liquidity-adjusted futures pricing model compared with traditional future pricing model without liquidity factor. I utilize mean pricing error (MPE) and root mean squared error (RMSE) to estimate errors for both models and I also adopt T-test for statistical significance justifications. For most years, liquidity adjusted futures pricing model performs better than the traditional model with results being statistically significant.
More importantly, liquidity adjusted futures pricing model can predict spot prices and futures prices simultaneously, which means only one model can be applied in both spot price predictions and futures price predictions based purely on historical market information. Existing models either predict futures prices by using spot prices (e.g. Black, 1976) or use futures prices to predict spot prices (e.g. Reichsfeld and Roache, 2011). As a result, my model has a great degree of prediction power with its prediction errors being less than 3\%, which is relatively small.
Therefore, it is arguable, that liquidity risk plays a key role in commodity futures markets and illiquidity of those assets could prove influential on firms' daily operations. I also build an intrinsic nexus between real options theory and real asset illiquidity to accommodate this issue. Study of the new real options model reveals effects of real asset illiquidity towards investment threshold and flexibility values, namely, exercise boundary and real options values, which is complementary to existing real options and corporate finance literatures. Instead of constructing free boundary line, which shows effects of time and asset price, the model presents a three-dimensional ‘free surface’, which indicates not only effects of time and asset price, but also that of asset illiquidity.
The new model contributes to two types of existing literatures. The first type focuses on effects of real asset illiquidity (mainly physical asset) on corporate investment and cost of capital. Illiquidity of existing physical assets will decrease corporate investment and increase cost of capital (Gan, 2007; Flor and Hirth, 2013, and Ortiz-Molina and Phillips, 2014). In addition to physical asset illiquidity, I distinguish physical asset from (expected) inventory asset within real asset category. The new model shows that the inventory asset illiquidity would also shape the corporate investment behaviors.
Additionally, the model also relates to literatures that document investment booming during unfavourable market conditions. I argue real asset illiquidity could engender the suboptimal exercise of real options. Simulation results of the new model illustrate that investment threshold becomes lower as waiting value and flexibility get eroded by asset illiquidity. Because of lower exercising boundaries, firms have higher a probability to exercise real options, but at lower values, which results in suboptimal exercise of real options. The suboptimal exercise of the real options due to the asset illiquidity might provide an interpretation for the investment booming during unfavourable market conditions. More importantly, I argue that the suboptimal exercise of real options might undermine firm value and thus firms shall be more prudent to invest when the environment is unfriendly.
|Date of Award
|19 Apr 2016
- Univerisity of Nottingham
|Yongmin Zhang (Supervisor) & David Newton (Supervisor)