Medium-term Horizon Research Articles

Labor Income Risk and Stock Returns: The Role of Horizon Effects

This paper shows that the impact of labor income risk on the cross-section of expected stock returns depends crucially on the horizon. Using a flexible empirical approach that allows us to include multiple horizons simultaneously, we find robust evidence that the two- to four-year horizon strongly dominates. Labor income risk at this medium term horizon carries a highly significant price of risk, while at other horizons it does not. A simple two-factor model that includes the equity market return and labor income risk at the medium term horizon can explain a striking 71% of the cross-sectional variation of 25 size book-to-market and 25 size-investment portfolios. This is a significant improvement over the standard human capital CAPM with quarterly labor income growth that has an R-squared of only 7%. Also, labor income risk generates significant adjustments to the composition of the optimal risky equity portfolio at the medium term horizon. These results are consistent with wage stickiness, where wages are reset every two to four years.

Approximate analytical solutions for consumption/investment problems under recursive utility and finite horizon

We study the asset allocation and consumption decisions of an investor with recursive utility and a finite investment horizon. We provide an approximate analytical solution under a stochastic investment opportunity set. The solution becomes exact when the elasticity of intertemporal substitution is equal to one or under a constant opportunity set. We show that this elasticity impacts both consumption and portfolio strategies, indicating the importance of disentangling intertemporal substitution from risk aversion. The investor’s horizon also plays a crucial role in optimal policies and the usual infinite horizon framework is inappropriate for investors having short- or medium-term horizons. Moreover, the infinite horizon problem reveals the existence of conditions on the preference parameters for our solution to hold, raising the question of whether another solution may exist or not. On its turn, the absence of a bequest motive in the finite horizon problem imposes another condition on risk parameters.

Forecasting of electricity demand in the region

Ongoing structural changes on the electricity market and technological development affecting consumers and producers increase uncertainty concerning demand for electricity, even in a short-time horizon. Because of this, it is necessary to develop forecasting methods. This paper presents a method for forecasting electricity demand based on the idea of an end-use model. Such models use regional electricity consumption rates and population growth predictions as input data, on the basis of which they yield electricity demand forecast for the whole country and for particular regions. The model also deploys stochastic differential equations for simulating time-variation of electricity consumption rates by means of the Euler method. On the basis of available statistical data, the results of a forecast in a medium-term horizon are presented.

Electricity price forecasting in the short term hybridising fundamental and econometric modelling

Traders and practitioners in diverse power exchanges are nowadays being most exposed to uncertainty than ever. The combination of several factors such as renewable generation and regulatory changes calls for suitable electricity price forecasting models that can deal with complex and unusual market conditions. Several authors have proposed combining fundamental approaches with econometric models in order to cover all relevant aspects for electricity price forecasting. This combination has shown positive results for medium-term horizons. However, this approach has rarely been carried out for short-term applications. Moreover, several day-to-day applications in electricity markets require fast responsiveness and accurate forecasts. All of these facts encourage this work’s short-term hybrid electricity price forecasting model, which combines a cost-production optimisation (fundamental) model with an artificial neural network (econometric) model. In order to validate the advantages and contributions of the proposed model, it has been applied to a real-size power exchange with complex price dynamics, such as the Iberian electricity market. Moreover, its forecasting performance has been compared with those of the two individual components of the hybrid model as well as other well-recognised methods. The results of this comparison prove that the proposed forecasting model outperforms the benchmark models, especially in uncommon market circumstances.

Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management

In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are – multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg–Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is practiced calculating the correlation between actual cooling load demand and input feature variables of climate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation (R); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model's performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited number of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction.

Программа первоочередных действий по социально-экономическому возрождению Кузбасса

<span>Статья посвящена социально-экономическому развитию Кузбасса в кратко- и среднесрочной перспективе. Проанализированы основные проблемы и экономический потенциал региона. Предложена программа первоочередных действий, нацеленная на прорыв в экономике и социальной сфере региона в течение ближайших лет. Сформулированы цели, основные направления и мероприятия программы. Приведены прогнозные оценки для целевого сценария развития Кузбасса, полученные в рамках модельных расчетов.</span>

Unwinding External Stock Imbalances? The Case of Italy’s Net International Investment Position

This paper is a case study of an (almost complete) adjustment of Italy’s external stock imbalance. After reaching a peak of around 25 per cent of GDP in early 2014, Italy’s net external debtor position has steadily decreased, reaching less than 7 per cent of GDP at the end of 2017. The contribution of this work is twofold. First, it reviews the main developments in Italy’s net international investment position (NIIP) since 1999. Second, it reports a baseline projection of Italy’s NIIP over a medium-term horizon, as implied by current account balance forecasts. Since this projection ignores the role of valuation adjustments, the study also provides an analysis of their sensitivity to a set of potential movements in exchange rates and equity or bond markets.

Is Defense Spending Inflationary? Time–frequency Evidence from China

This study examines the nexus between the defense spending growth rate and inflation to test whether defense spending is inflationary in China, particularly in times of peace. Using wavelet analysis, we find that the linkage between the defense spending growth rate and inflation changes over time and across frequencies. We provide evidence supporting negative leading effects of inflation on the defense spending growth rate in certain periods across various frequencies. The results illustrate that the relationship is stronger at the short- to medium-term horizon of less than eight years than it is in the long term. No positive leading effect of defense spending on inflation is demonstrated, showing that defense spending is not inflationary in China. The results indicate that inflation lowers the defense spending growth rate at short and medium scales, particularly in peace time. This study provides new insights into the nexus between defense spending and inflation and emphasizes that such a correlation has time and frequency features. Meanwhile, given that there is little evidence supporting the idea that defense spending is inflationary, a moderate increase in defense spending will not damage price stability in China.

Predicting Market Reactions to Bad News

Our Applied Finance Project aims to develop a framework to predict short-term and medium-term market reactions to bad news shocks. The study is based on a sample of 18,497 bad news articles and time series of 1,008 Russell 3000 stocks returns during the period 2005 to 2017. Our research proposes a three-stage model for the analysis. Firstly, given a dataset of bad news events and stock prices, we employ time series clustering techniques on cumulative abnormal returns of stocks, by which the news articles related to those stocks are grouped into different clusters. Secondly, we apply Natural Language Processing and multi-class classification algorithms on relevant news articles to extract features of each cluster. Then, by applying Support Vector Machine model, whenever specific bad news is released, we can predict the subsequent short-term, and medium-term market reactions post negative news. Finally, we develop long/short trading strategy for both short-term and medium-term horizons that asset managers in the real world can apply every day.

Modelo de Simulación Jerárquico para la Proyección Espacio Temporal de la Demanda Eléctrica: caso de estudio en CENTROSUR

Este artículo propone un modelo de Proyección Espacial de la Demanda (PED) aplicado a un sistema eléctrico de distribución para el corto, mediano y largo plazo, lo cual incluye la estimación de la magnitud y la ubicación geográfica de la demanda de energía eléctrica de nuevos clientes.
 El modelo propuesto es un híbrido tendencia-simulación y utiliza una estructura jerárquica: desde abajo hacia arriba para adicionar y analizar datos; y, desde arriba hacia abajo para asignar el crecimiento de carga en sub-áreas y micro-áreas. El enfoque de pequeñas áreas es combinado con modelos de regresión de series espacio-tiempo y análisis de tendencia, en grandes regiones. Se utilizan factores locales, de proximidad y contorno para crear un mapa de preferencias. En cada región, se asigna el crecimiento de clientes en función de los valores del mapa de preferencias y una técnica autómata celular, luego se combina este uso del suelo con los datos de la curva de carga y así se obtiene las cargas en estas pequeñas áreas.
 Hemos probado el modelo de PED con el sistema de distribución de CENTROSUR; el resultado es un mapa de densidad de demanda que muestra las áreas más probables donde se asignaron los nuevos clientes, proporcionando así información sobre dónde, cuánto y cuándo cambiará la demanda, con el suficiente detalle y precisión requerida.
 Este artículo propone un modelo de Proyección Espacial de la Demanda (PED) aplicado a un sistema eléctrico de distribución para el corto, mediano y largo plazo, lo cual incluye la estimación de la magnitud y la ubicación geográfica de la demanda de energía eléctrica de nuevos clientes.
 El modelo propuesto es un híbrido tendencia-simulación y utiliza una estructura jerárquica: desde abajo hacia arriba para adicionar y analizar datos; y, desde arriba hacia abajo para asignar el crecimiento de carga en sub-áreas y micro-áreas. El enfoque de pequeñas áreas es combinado con modelos de regresión de series espacio-tiempo y análisis de tendencia, en grandes regiones. Se utilizan factores locales, de proximidad y contorno para crear un mapa de preferencias. En cada región, se asigna el crecimiento de clientes en función de los valores del mapa de preferencias y una técnica autómata celular, luego se combina este uso del suelo con los datos de la curva de carga y así se obtiene las cargas en estas pequeñas áreas.
 Hemos probado el modelo de PED con el sistema de distribución de CENTROSUR; el resultado es un mapa de densidad de demanda que muestra las áreas más probables donde se asignaron los nuevos clientes, proporcionando así información sobre dónde, cuánto y cuándo cambiará la demanda, con el suficiente detalle y precisión requerida.

Machine Learning at Central Banks

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and limitations. We review the underlying technical sources and the nascent literature applying machine learning to economic and policy problems. We present popular modelling approaches, such as artificial neural networks, tree-based models, support vector machines, recommender systems and different clustering techniques. Important concepts like the bias-variance trade-off, optimal model complexity, regularisation and cross-validation are discussed to enrich the econometrics toolbox in their own right. We present three case studies relevant to central bank policy, financial regulation and economic modelling more widely. First, we model the detection of alerts on the balance sheets of financial institutions in the context of banking supervision. Second, we perform a projection exercise for UK CPI inflation on a medium-term horizon of two years. Here, we introduce a simple training-testing framework for time series analyses. Third, we investigate the funding patterns of technology start-ups with the aim to detect potentially disruptive innovators in financial technology. Machine learning models generally outperform traditional modelling approaches in prediction tasks, while open research questions remain with regard to their causal inference properties.

The dependence structure across oil, wheat, and corn: A wavelet-based copula approach using implied volatility indexes

This paper examines the dependence structure between three commodities implied volatility indexes (oil, wheat and corn) during bear, normal and bull markets and at different scales. For this purpose, we combine wavelet and copula methods to analyse the changes of the tail dependence at different scales or investment horizons. The results support evidence of time-varying asymmetric tail dependence between the pair of cereals as well as between oil and the two cereals at different time horizons – short-term horizon, medium term horizon and long term horizon, suggesting that the dependence structure is sensitive to time horizons. These results have important implications for the analysis of portfolio risk management.

An interactive cooperation model for neighboring virtual power plants

Future distribution systems will accommodate an increasing share of distributed energy resources (DERs). Facing with this new reality, virtual power plants (VPPs) play a key role to aggregate DERs with the aim of facilitating their involvement in wholesale electricity markets. In this paper, the trading strategies of a VPP in cooperation with its neighboring VPPs are addressed. Toward this aim, a portfolio of inter-regional contracts is considered to model this cooperation and maximize the energy trade opportunities of the VPP within a medium-term horizon. To hedge against profit variability caused by market price uncertainties, two efficient risk management approaches are also implemented in the VPP decision-making problem based on the concepts of conditional value at risk (CVaR) and second-order stochastic dominance constraints (SSD). The resulting models are formulated as mixed-integer linear programming (MILP) problems that can be solved using off-the-shelf software packages. The efficiency of the proposed risk-hedging models is analyzed through a detailed case study, and thereby relevant conclusions are drawn.

Capacity management of modular assembly systems

Companies handling large product portfolio often face challenges that stem from market dynamics. Therefore, in production management, efficient planning approaches are required that are able to cope with the variability of the order stream to maintain the desired rate of production. Modular assembly systems offer a flexible approach to react to these changes, however, there is no all-encompassing methodology yet to support long and medium term capacity management of these systems. The paper introduces a novel method for the management of product variety in assembly systems, by applying a new conceptual framework that supports the periodic revision of the capacity allocation and determines the proper system configuration. The framework has a hierarchical structure to support the capacity and production planning of the modular assembly systems both on the long and medium term horizons. On the higher level, a system configuration problem is solved to assign the product families to dedicated, flexible or reconfigurable resources, considering the uncertainty of the demand volumes. The lower level in the hierarchy ensures the cost optimal production planning of the system by optimizing the lot sizes as well as the required number of resources. The efficiency of the proposed methodology is demonstrated through the results of an industrial case study from the automotive sector.

An impact evaluation of investment in infrastructure: The case of a railway connection in Uzbekistan

The Tashguzar–Baysun–Kumkurgan (TBK) railway connection, completed in 2007 in southern Uzbekistan, provided a domestic through line to link rail stretches formerly connected only via neighboring Turkmenistan. We hypothesize that a better integrated domestic rail system would yield economic spillovers beyond the regions actually traversed by the new connector to generate higher growth for neighboring regions and even for the far terminal reaches of the system. We test for this using a difference in difference approach that assesses the impact of the rail connector on economic performance for each of three regional groupings relative to a control group of unaffected regions. We further distinguish effects for the short term (2008), medium term (2009–10), and long term (2011–12). We find statistically significant increases in GDP growth for all three regional groupings at medium or long term horizons with magnitudes of increase up to 2.6 percentage points per year.

Demand-Driven Structural Change in Applied General Equilibrium Models

This study analyzes the variations in industrial structure induced by income-sensitive patterns of final consumption, and how these changes can be captured by a multi-sector numerical model with a flexible demand system. We focus, in particular, on the estimation of parameters for an AIDADS (An Implicitly, Directly Additive Demand System) specification. We then test the latter by inserting it in the ENVISAGE global general equilibrium dynamic model, which is run under the SSP2 scenario from 2011 to 2050. It is found that time-varying income elasticity can generate sizable variations in the industrial structure. This finding has important practical implications, particularly when structural models are applied at a medium and long term horizon.

Modeling an Optical Network Operating with Hybrid-Switching Paradigms

The continuous increasing of the Internet traffic has brought serious concerns about capacity exhaustion of the optical infrastructure in a medium term horizon. Taking into account the optical networks, comprising the Internet backbone, the strategies to avoid this catastrophic scenario point to the development of new technologies that enable the capacity expansion, as well as to a better utilization of network resources (spectrum or wavelengths). The elastic optical network, considered as a realistic perspective for the infrastructure, optimizes the required spectrum and improve the efficiency of resources utilization. However, it is based on optical circuit switching paradigm that tends to waste resources due to idle times of reservation process. Since in some dedicated optical networks, like data center networks, certain optical switching paradigms have become reality, it is expected that in the coming years not only an elastic optical network, but also a hybrid network, in terms of switching paradigms, be brought to reality. Considering this scenario, the present paper addresses some issues on the modeling of an optical network working with hybrid-switching paradigms.

Menová politika záporných úrokových sadzieb v eurozóne a Japonsku

Since 2014, European Central Bank and Bank of Japan have entered into the unchartered water of modest negative nominal interest rates, when they broke zero lower bound. Large-scale asset purchases and forward guidance effects are limited by zero nominal bound if yield curve is absolutely flat. Negative interest rate has been implemented with the aim of reinforcing effects of unconventional monetary policy and lowering real interest rates. Therefore, another decrease in nominal yield curve should ensure, that inflation target will be met in a medium term horizon. Main goal of the paper is to reveal implementation, transmission channels and risks associated with the negative interest rates. Implementation was successful within the existing framework of the current monetary policy. Negative effective interest rate has been reflected in sharp drop of governments yield curve and mortgage rates. According current constellation of negative interest rates, monetary effect outperforms potential unintended impacts. On the other side, there are many risks mainly for financial stability and financial market functioning, if negative interest rates will be under effective zero lower bound or will persists for an extended period.

A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

Short-term load forecasting is essential for reliable and economic operation of power systems. Short-term forecasting covers a range of predictions from a fraction of an hour-ahead to a day-ahead forecasting. An accurate load forecast results in establishing appropriate operational practices and bidding strategies, as well as scheduling adequate energy transactions. This paper presents a generalized technique for modeling historical load data in the form of time-series with different cycles of seasonality (e.g., daily, weekly, quarterly, annually) in a given power network. The proposed method separately models both non-seasonal and seasonal cycles of the load data using auto-regressive (AR) and moving-average (MA) components, which only rely on historical load data without requiring any additional inputs such as historical weather data (which might not be available in most cases). The accuracy of data modeling is examined using the Akaike/Bayesian information criteria (AIC/BIC) which are two effective quantification methods for evaluation of data forecasting. In order to validate the effectiveness and accuracy of the proposed forecaster, we use the hourly-metered load data of PJM network as a real-world input dataset.

Dynamic Vector Model Applied to Wind Speed Prognosis for Eolic Generation

Dynamic characterization of energy availability profiles is paramount for an adequate incorporation of Non-Conventional Renewable Energies. This fact is particularly significant for sizing and design of eolic energy parks. The integration of eolic parks with interconnected systems requires accurate and precise knowledge on maximum and minimum power availability, as well as the moments in which you should expect the aforementioned conditions. Prognosis tools can help to determine the wind speed with a certain degree of reliability, in order to forecast energy availability. In this regard, this article aims at designing and implementing a methodology to generate a dynamic vector-autoregressive-based models for wind speed prognosis. This methodology makes use of techniques such as data clustering, time series statistical analysis and its characterization through time-variant parametric models, for a medium term horizon. The proposed method is able to prognosticate wind speed for a complete day in just one step, instead of classic approaches that repeat several one-step ahead transitions to obtain similar results. The employed methodology facilitates the identification of periodical components of the wind, including daily and seasonal, facilitating the differentiation of data clusters with similar behaviors or tendencies. In order to perform the clustering, seasonal patterns are distinguishable through the use of similar probability distributions. Kullback-Leibler divergence is used as a measure of the difference between the probability distributions, while the K-means algorithm is used for clustering. Finally, for the validation of the design two common methods are implemented: Nielsen Reference Model and an ARMA-GARCH model. Our comparative analysis shows that the proposed method greatly improves the precision and accuracy of the resulting wind forecasting.