Forecasting long memory series subject to structural change: A two-stage approach

A two-stage forecasting approach for long memory time series is introduced. In the first step, we estimate the fractional exponent and, by applying the fractional differencing operator, obtain the underlying weakly dependent series. In the second step, we produce multi-step-ahead forecasts for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to both stationary and nonstationary cases. Simulations and an application to seven time series provide evidence that the new methodology is more robust to structural change and yields good forecasting results

Philanthropy or solidarity? Ethical dilemmas about humanitarianism in crisis afflicted Greece

That philanthropy perpetuates the conditions that cause inequality is an old argument shared by thinkers such as Karl Marx, Oscar Wilde and Slavoj Zizek. I recorded the same argument in conversations regarding a growing humanitarian concern in austerity-ridden Greece. At the local level a number of solidarity initiatives provide the most impoverished families with humanitarian help. Some citizens participate in such initiatives wholeheartedly, while some other citizens criticize solidarity movements drawing primarily from Marxist-inspired arguments, such as, for example, that humanitarianism rationalises state inaction. The local narratives presented in this article bring forward two parallel possibilities engendered by the humanitarian face of social solidarity: first, its empowering potential (where solidarity initiatives enhance local social awareness), and second, the de-politicisation of the crisis and the experience of suffering (a liability that stems from the effectiveness of humanitarianism in ameliorating only temporarily the superficial consequences of the crisis). These two overlapping possibilities can help us problematise the contextual specificity and strategic employment of humanitarian solidarity in times of austerity

A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing

Marine transport has grown rapidly as the result of globalization and sustainable world growth rates. Shipping market risks and uncertainty have also grown and need to be mitigated with the development of a more reliable procedure to predict changes in freight rates. In this paper, we propose a new forecasting model and apply it to the Baltic Dry Index (BDI). Such a model compresses, in an optimal way, information from the past in order to predict freight rates. To develop the forecasting model, we deploy a basic set of predictors, add lags of the BDI and introduce additional variables, in applying Bayesian compressed regression (BCR), with two important innovations. First, we include transition functions in the predictive set to capture both smooth and abrupt changes in the time path of BDI; second, we do not estimate the parameters of the transition functions, but rather embed them in the random search procedure inherent in BCR. This allows all coefficients to evolve in a time-varying manner, while searching for the best predictors within the historical set of data. The new procedures predict the BDI with considerable success

Return signal momentum

A new type of momentum based on the signs of past returns is introduced. This momentum is driven primarily by sign dependence, which is positively related to average return and negatively related to return volatility. An empirical application using a universe of commodity and financial futures offers supporting evidence for the existence of such momentum. Investment strategies based on return signal momentum result in higher returns and Sharpe ratios and lower drawdown relative to time series momentum and other benchmark strategies. Overall, return signal momentum can benefit investors as an effective strategy for speculation and hedging. © 2021 Elsevier B.V

An Automatic Leading Indicator, Variable Reduction and Variable Selection Methods Using Small and Large Datasets: Forecasting the Industrial Production Growth for Euro Area Economies

This paper assesses the forecasting performance of various variable reduction and variable selection methods. A small and a large set of wisely chosen variables are used in forecasting the industrial production growth for four Euro Area economies. The results indicate that the Automatic Leading Indicator (ALI) model performs well compared to other variable reduction methods in small datasets. However, Partial Least Squares and variable selection using heuristic optimisations of information criteria along with the ALI could be used in model averaging methodologies

Singular spectrum analysis for value at risk in stochastic volatility models

[EN] Estimation of the value at risk (VaR) requires prediction of the future volatility. Whereas this is a simple task in ARCH and related models, it becomes much more complicated in stochastic volatility (SV) processes where the volatility is a function of a latent variable that is not observable. In-sample (present and past values) and out-of-sample (future values) predictions of that unobservable variable are thus necessary. This paper proposes singular spectrum analysis (SSA), which is a fully nonparametric technique that can be used for both purposes. A combination of traditional forecasting techniques and SSA is also considered to estimate the VaR. Their performance is assessed in an extensive Monte Carlo and with an application to a daily series of S&P500 returns.Basque Government, Grant/Award Number: IT1359-19; Spanish Ministry of Science and Innovation and ERDF, Grant/Award Number: ECO2016-76884-P; National Research Agency, Grant/Award Number: PID2019-105183GB-I0