This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...

In this paper we present a method for calculating the entire hedge surface of a derivative who’s future underlying asset has been simulated by a market simulator for example with the Monte Carlo method. Our method is built from work on penalized filtering techniques and is applied on a grid of...

The volatility of concern in conventional volatility-managed strategies such as volatility-targeting strategy and mean-variance optimization is the expected conditional volatility. However for investors, it is the realized volatility that is important, because there is only one realization in...

The popular conditional autoregressive Wishart (CAW) model for dynamics of realized covariance matrices provides a flexible parametrisation. However, the number of parameters grows quadratically with the number of assets, which causes enormous computational difficulties in higher dimensions....

This document provides an overview of the StMAR Toolbox, a MATLAB toolbox specifically designed for simulation, estimation, diagnostic, and forecasting of the Student's t mixture autoregressive (StMAR) model proposed by Meitz, Preve & Saikkonen (2018). The StMAR model is a new type of mixture...

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

This paper discusses solution procedures for real business cycle (RBC) models. First, we show that the most often used solution methods, the linear-quadratic approximation, the Lagrange multiplier, and the Euler equation approach all lead to the same decision function. Second, we demonstrate...

We consider a class of infinite‐horizon dynamic Markov economic models in which the parameters of utility function, production function, and transition equations change over time. In such models, the optimal value and decision functions are time‐inhomogeneous: they depend not only on state...

The standard model of the small open economy is saddled with a unit root that greatly complicates numerical computation of the global saddle path. In this paper we solve the unit root problem by developing a set of innovative forward-shooting algorithms. Exploiting the fact that the algorithms...

This paper analyses the contribution of various numerical approaches to making the estimation of threshold autoregressive time series more efficient. It relies on the computational advantages of QR factorizations and proposes Givens transformations to update these factors for sequential LS...