We introduce an asset-allocation framework based on the active control of the value-at- risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with...

Many financial applications, such as risk analysis and derivatives pricing, depend on time scaling of risk. A common method for this purpose, though only correct when returns are iid normal, is the square–root–of–time rule where an estimated quantile of a return distribution is scaled to a...

We provide an equilibrium multi-asset pricing model with micro-founded systemic risk and heterogeneous investors. Systemic risk arises due to excessive leverage and risk taking induced by free-riding externalities. Global risk-sensitive financial regulations are introduced with a view of...

Most financial risk regulations assume that asset returns are exogenous, where risk is estimated from historical data. This assumption fails to take into account the feedback effect of trading decisions on prices. We investigate the consequences of risk constrained trading by means of...

We provide an equilibrium multi-asset pricing model with micro- founded systemic risk and heterogeneous investors. Systemic risk arises due to excessive leverage and risk taking induced by free-riding externalities. Global risk-sensitive financial regulations are introduced with a view of...