The R package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference

This paper presents the R-package MitISEM (mixture of t by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture of Student-t densities as approximating density. In the first stage a mixture of Student-t densities is fitted to the target using an expectation maximization (EM) algorithm where each step of the optimization procedure is weighted using importance sampling. In the second stage this mixture density is a candidate density for efficient and robust application of importance sampling or the Metropolis-Hastings (MH) method to estimate properties of the target distribution. The package enables Bayesian inference and prediction on model parameters and probabilities, in particular, for models where densities have multi-modal or other non-elliptical shapes like curved ridges. These shapes occur in research topics in several scientific fields. For instance, analysis of DNA data in bio-informatics, obtaining loans in the banking sector by heterogeneous groups in financial economics and analysis of education's effect on earned income in labor economics. The package MitISEM provides also an extended algorithm, 'sequential MitISEM', which substantially decreases computation time when the target density has to be approximated for increasing data samples

Portfolio Re-Balancing and Optimization Using Directional Changes and Genetic Algorithms

Dynamic portfolio optimization is a crucial but complex task due to financial market dynamics and the difficulty of disentangling noise from substantial changes in stock prices. In most existing methods, portfolios are re-optimized, hence re-balanced, at pre-specified time periods, return properties of each asset are dynamically computed, and portfolio weights are optimized according to an objective function. We propose a novel algorithm for dynamic portfolio optimization with a two-step signaling mechanism for re-balancing the portfolio including the optimization of re-balancing points and portfolio weights. The first step signals portfolio re-balancing only if there is a substantial price change in one or more of the portfolio constituents. These substantial price changes are defined according to directional change (DC) methods. DC methods create an intrinsic time series for each asset according to whether or not the change in the asset price exceeds a threshold level, hence removing part of the noise in asset prices. The second signaling mechanism uses genetic algorithms (GA) to assess if re-balancing is indeed profitable at each point indicated by the first signaling mechanism. The genetic algorithm is set up such that it simultaneously optimizes the weights of the re-balanced portfolio. For GA, we input the asset price summaries retrieved from DC methods to ensure that the GA can learn from the relatively less noisy data compared to observed asset prices. We show that the GA fit function can be set up to include several conventional trading strategies. As a first step, we apply the proposed method to a portfolio of 30 assets including 29 Exchange Traded Funds (ETF) and one risk-free asset where daily prices are observed during the period between 2 January 2018 and 30 December 2021. Second, we apply the method to 100 individual stocks for the same time period. We compare the obtained portfolio results with benchmarks, such as the simple buy and hold strategy of the S&P 500 index, the naive $1/N$ portfolio, and a minimum variance portfolio in terms of standard portfolio evaluation methods including the Sharpe ratio

Structural differences in economic growth: An endogenous clustering approach

This article addresses heterogeneity in determinants of economic growth in a data-driven way. Instead of defining groups of countries with different growth characteristics a priori, based on, for example, geographical location, we use a finite mixture panel model and endogenous clustering to examine cross-country differences and similarities in the effects of growth determinants. Applying this approach to an annual unbalanced panel of 59 countries in asia, latin and middle america and africa for the period 1971–2000, we can identify two groups of countries in terms of distinct growth structures. The structural differences between the country groups mainly stem from different effects of investment, openness measures and government share in the economy. Furthermore, the detected segmentation of countries does not match with conventional classifications in the literature

Multivariate Density Estimation by Neural Networks

We propose nonparametric methods to obtain the Probability Density Function (PDF) to assess the properties of the underlying data generating process (DGP) without imposing any assumptions on the DGP, using neural networks (NNs). The proposed NN has advantages compared to well-known parametric and nonparametric density estimators. Our approach builds on literature on cumulative distribution function (CDF) estimation using NN. We extend this literature by providing analytical derivatives of this obtained CDF. Our approach hence removes the numerical approximation error in differentiating the CDF output, leading to more accurate PDF estimates. The proposed solution applies to any NN model, i.e., for any number of hidden layers or hidden neurons in the multilayer perceptron (MLP) structure. The proposed solution applies the PDF estimation by NN to continuous distributions as well as discrete distributions. We also show that the proposed solution to obtain the PDF leads to good approximations when applied to correlated variables in a multivariate setting. We test the performance of our method in a large Monte Carlo simulation using various complex distributions. Subsequently, we apply our method to estimate the density of the number of vehicle counts per minute measured with road sensors for a time window of 24 h