Mineralising Software: Moving Through Material Processes

Introducing the notion of ‘mineralisation’ as a conceptual tool taken from Manuel DeLanda and applied in the context of software, this article investigates the mineral origins of software and how to engage in mapping the vibrancy of software through new material practices

A profit model for spread trading with an application to energy futures

This paper proposes a profit model for spread trading by focusing on the stochastic movement of the price spread and its first hitting time probability density. The model is general in that it can be used for any financial instrument. The advantage of the model is that the profit from the trades can be easily calculated if the first hitting time probability density of the stochastic process is given. We then modify the profit model for a particular market, the energy futures market. It is shown that energy futures spreads are modeled by using a meanreverting process. Since the first hitting time probability density of a mean-reverting process is approximately known, the profit model for energy futures price spreads is given in a computable way by using the parameters of the process. Finally, we provide empirical evidence for spread trades of energy futures by employing historical prices of energy futures (WTI crude oil, heating oil, and natural gas futures) traded on the New York Mercantile Exchange. The results suggest that natural gas futures trading may be more profitable than WTI crude oil and heating oil due to its high volatility in addition to its long-term mean reversion, which offers supportive evidence of the model prediction. --futures spread trading,energy futures markets,mean-reverting process,first hitting,time probability density,profit model,WTI crude oil,heating oil,natural gas

CVaR sensitivity with respect to tail thickness

We consider the sensitivity of conditional value-at-risk (CVaR) with respect to the tail index assuming regularly varying tails and exponential and faster-than-exponential tail decay for the return distribution. We compare it to the CVaR sensitivity with respect to the scale parameter for stable Paretian, the Student's t, and generalized Gaussian laws and discuss implications for the modeling of daily returns and marginal rebalancing decisions. Finally, we explore empirically the impact on the asymptotic variability of the CVaR estimator with daily returns which is a standard choice for the return frequency for risk estimation. --fat-tailed distributions,regularly varying tails,conditional value-at-risk,marginal rebalancing,asymptotic variability

On a class of distributions stable under random summation

We investigate a family of distributions having a property of stability-under-addition, provided that the number $\nu$ of added-up random variables in the random sum is also a random variable. We call the corresponding property a \,$\nu$-stability and investigate the situation with the semigroup generated by the generating function of $\nu$ is commutative. Using results from the theory of iterations of analytic functions, we show that the characteristic function of such a $\nu$-stable distribution can be represented in terms of Chebyshev polynomials, and for the case of $\nu$-normal distribution, the resulting characteristic function corresponds to the hyperbolic secant distribution. We discuss some specific properties of the class and present particular examples.Comment: 12 pages, 1 figur

Estimation of operational value-at-risk in the presence of minimum collection threshold: An empirical study

The recently finalized Basel II Capital Accord requires banks to adopt a procedure to estimate the operational risk capital charge. Under the Advanced Measurement Approaches, that are currently mandated for all large internationally active US banks, require the use of historic operational loss data. Operational loss databases are typically subject to a minimum recording threshold of roughly $10,000. We demonstrate that ignoring such thresholds leads to biases in corresponding parameter estimates when the threshold is ignored. Using publicly available operational loss data, we analyze the effects of model misspecification on resulting expected loss, Value-at-Risk, and Conditional Value-at-Risk figures and show that underestimation of the regulatory capital is a consequence of such model error. The choice of an adequate loss distribution is conducted via in-sample goodness-of-fit procedures and backtesting, using both classical and robust methodologies. --