Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

We extend the Black-Litterman framework beyond normality to general elliptical distributions of investor's views and asset returns and portfolio risk measured by CVaR. Unlike existing solutions, cf. Xiao and Valdez [Quant. Finan. 2015, 15:3, 509-519], the choice of distributions, with the first...

This chapter provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium (DSGE) models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field

This study presents a survey on the current methodologies employed in the asset allocation industry. We review techniques from Modern Portfolio Theory and focus on the central role of the mean-variance optimization problem. The formalism of the Black-Litterman model is discussed with its...

We estimate a small-scale macroeconomic model for Japan by taking into account the nonlinearity stemming from the zero lower bound (ZLB) of the nominal interest rate. To this end, we apply the Sequential Monte Carlo Squared method to the case of Japan, where the ZLB has constrained the country's...

We estimate a small-scale nonlinear DSGE model with the zero lower bound (ZLB) of the nominal interest rate for Japan, where the ZLB has constrained the country's monetary policy for a considerably long period. We employ the time iteration with linear interpolation method to solve equilibrium...

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where...

In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and...

This chapter provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.