Long memory options: LM evidence and simulations

This paper demonstrates the impact of the observed financial market persistence or long term memory on European option valuation by simple simulation. Many empirical researchers have observed the non-Fickian degrees of persistence in the financial markets different from the Fickian neutral independence (i.i.d.) of the returns innovations assumption of Black–Scholes’ geometric Brownian motion assumption. Moreover, Elliott and van der Hoek [Elliott, R.J., van der Hoek, J., 2003. A general fractional white noise theory and applications to finance. Math. Finance 13, 301–330] provide a theoretical framework for incorporating these findings into the Black–Scholes risk-neutral valuation framework. This paper provides the first graphical demonstration why and how such long term memory phenomena change European option values and provides thereby a basis for informed long term memory arbitrage. By using a simple mono-fractal fractional Brownian motion, it is easy to incorporate the various degrees of persistence into the Black–Scholes pricing formula. Long memory options are of considerable importance in corporate remuneration packages, since stock options are written on a company’s own shares for long expiration periods. It makes a significant difference in the valuation when an option is “blue” or when it is “red.” For a proper valuation of such stock options, the degrees of persistence of the companies’ share markets must be precisely measured and properly incorporated in the warrant valuation, otherwise substantial pricing errors may result.     

Achieving Attraction Accountability Through an Attraction Response Matrix

Whereas investments in new attractions continue to rise within the theme park industry, knowledge regarding the effects of new attractions on theme park performance and attendance remains scarce. In order to predict the impact of new attractions on the performance of European theme parks, this article presents an Attraction Response Matrix (ARM). The Attraction Response Matrix offers an integrated framework in which research into the effects of new attractions can take place in a systematic manner. The ARM attempts to transform post priori knowledge into a priori knowledge by better understanding the impact of a new attraction and its' mediating causes. The main premise of the ARM is: “in situation A, attraction B will most likely have effect C on target audience D.” By performing research into the relevant effects within certain cells of the ARM and consecutively investigating the relationship between the various cells, a better insight will be gained in the working of new attractions. ARM is based on an extensive ZMET study conducted in The Netherlands.     

Unravelling the JPMorgan spoofing case using particle physics visualization methods

On 29 September 2020, JPMorgan was ordered to pay a settlement of $920.2 million for spoofing the metals and Treasury futures markets from 2008 to 2016. We examine these cases using a visualization method developed in particle physics (CERN) and the messages that the exchange receives about market activity rather than time-based snapshots. This approach allows to examine multiple indicators related to market manipulation and complement existing research methods, thereby enhancing the identification and understanding of, as well as the motivation for, market manipulation. In the JPMorgan cases, we offer an alternative motivation for spoofing than moving the price.