This paper introduces Economic Space notion to expand capacity for economic and financial modeling. Introduction of Economic Space allows define economic variables as functions of time and coordinates and opens the way for treating economic and financial relations similar to mathematical physics...

This paper presents new approach to financial modeling and forecasting that is based on economic space notion. Economic space is defined as generalization of risk ratings and allows boost methods and description of financial processes. Risk ratings of economic agents are treated as coordinates...

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...

This paper studies option pricing based on a reverse engineering (RE) approach. We utilize artificial intelligence in order to numerically compute the prices of options. The data consist of more than 5000 call- and put-options from the German stock market. First, we find that option pricing...

This paper examines how including latent variables can benefit propensity score matching. A researcher can estimate, based on theoretical presumptions, the latent variable from the observed manifest variables and can use this estimate in propensity score matching. This paper demonstrates the...

In general, the properties of the conditional distribution of multiple period returns do not follow easily from the one-period data generating process. This renders computation of Value-at-Risk and Expected Shortfall for multiple period returns a non-trivial task. In this paper we consider some...

This paper develops the Regime Dependent Generalized Autoregressive Conditional Heteroskedasticity (RD-GARCH) model and applies it to a daily index of returns on U.S. equities. The RD-GARCH model is different from previous models in that it combines Hentschel's single specification that nests...

This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by the volatility dynamics dVt = κt.(θt − Vt).dt λt.Vt.dBt. This non-affine model is much more realistic than classical affine models like the Heston stochastic volatility...

In this paper we study the short term price behavior of December 2008 future prices for EU emissionallowances. We model returns and volatility dynamics of this price showing that a standard ARMA-GARCHframework is not adequate and that the gaussianity assumption is rejected due to the occurrence...

We define risk spillover as the dependence of a given asset variance on the past covariances and variances of other assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an international equity portfolio. According to the risk...