We show how distributions can be reduced to low-dimensional scenario trees. Applied to intertemporal distributions, the scenarios and their probabilities become time-varying factors. From S&P 500 options, two or three time-varying scenarios suffice to forecast returns, implied variance or...

VaR_Delta-Normal fails in two counts: subadditivity and potentially producing losses larger than its portfolio value. This paper solves the second inconsistency developing formulas derived from a put option, named PVaR_Delta-Normal and Put_Expected_Shortfall, PSF_Delta-Normal; the latter also...

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of...

Existing models of option returns neglect the distribution of expected underlying asset volatility. An ensemble of six common GARCH models affords estimates of the moments of the physical—rather than the risk-neutral—distribution of anticipated—instead of historical—volatility, as well...

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the...

In this paper I pose an old fashioned question, "Why do capitalist economies evolve in the way that they do?" The answer will lie, on the one hand, in the nature of human curiosity and the corresponding growth of knowledge and, on the other hand, in the particular instituted rules of the game...

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the...

In this paper we develop the first estimator of the covariance matrix that relies solely on forward-looking information. This estimator only uses price information from a cross-section of plain-vanilla options. In an out-of-sample study for US blue-chip stocks we show that a minimum-variance...