We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

The motivation of this paper is to introduce a short term adaptive model (Partial Swarm Optimizer combined with linear and nonlinear models when applied to the task of forecasting and trading the daily closing returns of the FTSE100 exchange traded funds (ETFs). This is done by benchmarking its...

Several exchanges in futures and options deploy pro-rata matching. The executed size of limit orders in pro-rata markets is never certain, unlike in price-time priority matching systems. This article derives the optimal size of limit orders in pro-rata markets given the trader's desired...

We present a simple method for “inverting” a volatility smile: that is, generating a CDF and inverse CDF given a discrete set of implied volatilities. The method is based on constructing a piece-wise linear CDF that is guaranteed to exactly reprice any non-arbitrageable input volatilities....