Abstract
<p> Presented at International Conference on Modern Management based on Big Data</p><p> <a href="http://www.mmbdconf.org/Speaker" target="_self"> Program </a></p><p> An alternative approach to the Black-Scholes-Merton formulation of option valuation is the entropy pricing theory. Entropy pricing applies notions of information theory to derive the theoretical value of options. I elaborate further on the maximum entropy formulation of option pricing using a generalized set of moment constraints. Higher order moments contain more information about the price density and characterize the shape of the underlying distribution. In a Monte Carlo study, I present entropies of heavy-tailed distributions and show that entropic call densities vary with constraints and become closer to each other as the order of moments increases. In an empirical analysis using high-frequency S&P 500 index options, I examine the impact of moment constraints on the accuracy of theoretical values. Simulation and empirical evidence suggest that the entropic pricing framework provides more accurate results for heavy-tailed, high-frequency data when higher order moment constraints are imposed.</p>
Original language | American English |
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State | Published - Oct 1 2020 |
Event | International Conference on Modern Management based on Big Data - Duration: Oct 1 2020 → … |
Conference
Conference | International Conference on Modern Management based on Big Data |
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Period | 10/1/20 → … |
DC Disciplines
- Business
- Economics