Forecasting
http://lists.repec.orgmailman/listinfo/nep-for
Forecasting
2016-06-14
Spline-DCS for Forecasting Trade Volume in High-Frequency Finance
http://d.repec.org/n?u=RePEc:cam:camdae:1606&r=for
We develop the spline-DCS model and apply it to trade volume prediction, which remains a highly non-trivial task in high-frequency finance. Our application illustrates that the spline-DCS is computationally practical and captures salient empirical features of the data such as the heavy-tailed distribution and intra-day periodicity very well. We produce density forecasts of volume and compare the model's predictive performance with that of the state-of-the-art volume forecasting model, named the component-MEM, of Brownlees et al. (2011). The spline-DCS significantly outperforms the component-MEM in predicting intra-day volume proportions.
Ryoko Ito
order slicing, price impact, robustness, score, VWAP trading
2016-01-24
Forward‐Looking USDA Price Forecasts
http://d.repec.org/n?u=RePEc:ags:aaea16:235931&r=for
USDA generates monthly season‐average price forecasts for key agricultural commodities. Uncertainty about each forecast is indicated by its publication as a price interval. USDA’s forecasting methodology is non‐public, but its uncertainty levels are anecdotally based on historical patterns of price uncertainty and informed by expert opinion. No confidence level is attached to USDA’s intervals, so it is difficult to gauge their accuracy. But in practice, realized season‐average prices regularly fall outside of USDA‐forecasted intervals, particularly those made prior to harvest and late in the marketing year. We demonstrate that forward‐looking density forecasts for the season‐average corn price can be constructed based on the market’s expectation of volatility implied by commodity options premia, combined with historical forecast errors between futures market prices and cash prices paid to farmers. Because implied volatility is forward‐looking, confidence intervals based on these densities reflect anticipatory market sentiment not present in historical data. In out‐of‐sample trials, our 95% confidence intervals contained the final season‐average price for over 92% of the 358 forecasts made between 1995/96 and 2014/15. Compared to a model based on historical data alone, the forward‐looking model is less susceptible to forecast errors. Our approach can enhance the informational value of USDA season‐average price forecasts.
Adjemian, Michael K.
Bruno, Valentina G.
Robe, Michel A.
USDA, derivatives markets, grains, forecasting, implied volatility, situation and outlook, WASDE, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Demand and Price Analysis, Financial Economics, Marketing, Risk and Uncertainty,
2016
Real-time forecasting with a MIDAS VAR
http://d.repec.org/n?u=RePEc:bof:bofitp:2015_013&r=for
This paper presents a MIDAS type mixed frequency VAR forecasting model. First, we propose a general and compact mixed frequency VAR framework using a stacked vector approach. Second, we integrate the mixed frequency VAR with a MIDAS type Almon lag polynomial scheme which is designed to reduce the parameter space while keeping models fexible. We show how to recast the resulting non-linear MIDAS type mixed frequency VAR into a linear equation system that can be easily estimated. A pseudo out-of-sample forecasting exercise with US real-time data yields that the mixed frequency VAR substantially improves predictive accuracy upon a standard VAR for dierent VAR specications. Forecast errors for, e.g., GDP growth decrease by 30 to 60 percent for forecast horizons up to six months and by around 20 percent for a forecast horizon of one year.
Mikosch, Heiner
Neuwirth, Stefan
Forecasting, mixed frequency data, MIDAS, VAR, real time
2015-04-13
Forecasting day-ahead electricity load using a multiple equation time series approach
http://d.repec.org/n?u=RePEc:qut:auncer:2015_01&r=for
The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the e ective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the methods are completely generic and applicable to any load forecasting problem. The model's forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia energy market operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.
Adam Clements
Stan Hurn
Zili Li
Short-term load forecasting, seasonality, intra-day correlation, recursive equation system
2014-09-06
Reading Between the Lines: Prediction of Political Violence Using Newspaper Text
http://d.repec.org/n?u=RePEc:cam:camdae:1630&r=for
This article provides a new methodology to predict conflict by using newspaper text. Through machine learning, vast quantities of newspaper text are reduced to interpretable topic shares. We use changes in topic shares to predict conflict one and two years before it occurs. In our predictions we distinguish between predicting the likelihood of conflict across countries and the timing of conflict within each country. Most factors identified by the literature, though performing well at predicting the location of conflict, add little to the prediction of timing. We show that news topics indeed can predict the timing of conflict onset. We also use the estimated topic shares to document how reporting changes before conflict breaks out.
Hannes Mueller
Christopher Rauh
Conflict, Forecasting, Machine Learning, Panel Data, Topic Models, Latent Dirichlet Allocation.
2016-05-04
Forecasting Agricultural Commodity Transportation Costs: Mississippi River Barge Rates
http://d.repec.org/n?u=RePEc:ags:aaea16:235947&r=for
Wetzstein, Brian
Florax, Raymond
Foster, Ken
Binkley, James
Marketing,
2016