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Fat tails arise endogenously from modeling of price change based on a quotient of arbitrarily correlated demand and supply (i.e., excess demand) whether or not jump discontinuities are present. The assumption is that supply and demand are described by drift terms, Brownian (i.e., Gaussians or normals) and compound Poisson jump processes. If $P^{-1}dP/dt$ (the relative price change in an interval $dt$) is given by a suitable function of excess demand, $\mathcal{D}/\mathcal{S}-1$ (where $\mathcal{D}$ and $\mathcal{S}$ are demand and supply), then the distribution has tail behavior $F\left( x\right) \sim x^{-\zeta}$ for a power $\zeta$ that depends on the function $G$ in $P^{-1}dP/dt=G\left( \mathcal{D}/\mathcal{S}\right) $. For $G\left( x\right) \sim\left\vert x\right\vert ^{1/q}$ one has $\zeta=q.$ The value, $q\in\left[ 3,5\right] $ is in agreement with empirical data. While many theoretical explanations have been offered for the paradox of fat tails, we show that this issue never arises if one models price dynamics using basic economics methodology, rather than the usual starting point for classical finance which assumes a normal distribution of price changes. The function, $G,$ can be calibrated in the absence of rare events. The results establish a simple link between the decay exponent of the density function and the price adjustment function, a feature that can improve methodology for risk assessment.

Fat Tails Arise Endogenously in Asset Prices From Supply/Demand, With or Without Jump Processes