Search results for "forecasting"
showing 10 items of 329 documents
Economic value, competition and financial distress in the european banking system
2012
Abstract In this paper we examine the impact of a large number of factors at the bank level (liquidity and credit risks, asset size, income diversification and market power), at the industry level (banking concentration) and macro-level (real GDP growth) on bank financial distress using an unbalanced panel of 308 European commercial banks between 1996 and 2009. The observations falling below a given threshold of the empirical distribution of the Shareholder Value Ratio proxy bank financial distress. We employ a panel probit regression and, given the presence of overlapping data giving rise to residual autocorrelation, we use the Bertschek and Lechner (1998) robust estimator of the covarianc…
Leading indicator properties of US high-yield credit spreads.
2010
Abstract In this paper we examine the out-of-sample forecast performance of high-yield credit spreads for real-time and revised data regarding employment and industrial production in the US. We evaluate models using both a point forecast and a probability forecast exercise. Our main findings suggest that the best results come from using only a few factors obtained by pooling information from a number of sector-specific high-yield credit spreads. In particular, for employment and at short-run horizons, there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks. Moreover, forecast results based on revised data …
A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
2008
The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest the use of the principal component methodology of Stock and Watson [Stock, J.H., Watson, M.W., 2002. Macroeconomic forecasting using diffusion indices. Journal of Business and Economic Statistics, 20, 147–162] for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard [Harvey, A.C., Ruiz, E., Shephard, N., 1994. Multivariate Stochastic Variance Models. Review of Economic Studies, 61, 247–264]. We provide theoretical and Monte Carlo results on this method and apply it to S&P data.
Forecasting Weekly Electricity Prices at Nord Pool
2007
This paper analyses the forecasting power of weekly futures prices at Nord Pool. The forecasting power of futures prices is compared to an ARIMAX model of the spot price. The time series model contains lagged external variables such as: temperature, precipitation, reservoir levels and the basis (futures price less the spot price); and generally reflects the typical seasonal patterns in weekly spot prices. Results show that the time series model forecasts significantly beat futures prices when using the Diebold and Mariano (1995) test. Furthermore, the average forecasting error of futures prices reveals that they are significantly above the settlement spot price at the ‘delivery week’ and th…
Training Artificial Neural Networks With Improved Particle Swarm Optimization
2020
Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is util…
An empirical comparison of cross-impact models for forecasting sales
1986
Abstract This paper compares a set of four cross-impact models: (1) additive, (2) likelihood multiplier, (3) R-space, and (4) a model constructed by the author. This is done by examining a forecasting problem encountered by an industrial firm. The forecasting problem was to study the market trend in order to decide whether to expand the production capacity of a ceramics plant. In spite of their different theoretical premises, the models yielded similar results. However, only the R-space model produced results that differed from the others. The paper also suggests a method that should avoid some internal contradictions of the cross-impact models.
External parameters contribution in domestic load forecasting using neural network
2015
Domestic demand prediction is very important for home energy management system and also for peak reduction in the power system network. In this work, for precise prediction of power demand, external parameters, such as temperature and solar radiation, are considered and included in the prediction model for improving prediction performance. Power prediction models for coming hours' power demand estimation are built using neural network based on hourly power consumptions data with / without ambient temperature data and global solar irradiation (GSI) data respectively. In this work, a typical Southern Norwegian household demand has been considered. As a result, both ambient temperature and GSI…
An expert system for vineyard management based upon ubiquitous network technologies
2011
Vineyard operations for quality wines production are currently based upon costly and time-consuming manual sampling operations required to assess the maturity phases of grapevines. The ripening process however is significantly influenced by the environmental parameters which nowadays can be effectively monitored by means of ubiquitous computing technologies. Besides the possibility of gathering data, hence, suitable tools are required to support the vineyard management process. The present research concerns the development of an expert system to effectively manage the vineyard operations. The methodology is based on the analysis of the time series of indices related to the maturation phases…
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by fir…
2017
In the deregulated competitive electricity market, the price which reflects the relationship between electricity supply and demand is one of the most important elements, making it crucial for all market participants to precisely forecast the electricity price. However, electricity price series usually has complex features such as non-linearity, non-stationarity and volatility, which makes the price forecasting turn out to be very difficult. In order to improve the accuracy of electricity price forecasting, this paper first proposes a two-layer decomposition technique and then develops a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition …
GA-ANN for Short-Term Wind Energy Prediction
2011
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation, frequency, voltage regulation within the statutory limits. Accurate wind energy forecasting helps the power system transmission system operators in anticipating rapid changes in wind turbine power output with respect to load and helps in making decision not only for optimum energy management bu…