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on Forecasting |
| By: | Tomasz Serafin; Bartosz Uniejewski; Rafal Weron |
| Abstract: | The recent developments in combining point forecasts of day-ahead electricity prices across calibration windows have provided an extremely simple, yet a very efficient tool for improving predictive accuracy. Here, we consider two novel extensions of this concept to probabilistic forecasting: one based on Quantile Regression Averaging (QRA) applied to a set of point forecasts obtained for different calibration windows, the other on a technique dubbed Quantile Regression Machine (QRM), which first averages these point predictions, then applies quantile regression to the combined forecast. Once computed, we combine the probabilistic forecasts across calibration windows by averaging probabilities of the corresponding predictive distributions. Our results show that QRM is not only computationally more efficient, but also yields significantly more accurate distributional predictions, as measured by the aggregate pinball score and the test of conditional predictive ability. Moreover, combining probabilistic forecasts brings further significant accuracy gains. |
| Keywords: | Electricity price forecasting; Predictive distribution; Combining forecasts; Average probability forecast; Calibration window; Autoregression; Pinball score; Conditional predictive ability |
| JEL: | C14 C22 C51 C53 Q47 |
| Date: | 2019–06–12 |
| URL: | http://d.repec.org/n?u=RePEc:ahh:wpaper:worms1908&r=all |
| By: | Oguzhan Cepni (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Ahmet Sensoy (Bilkent University, Faculty of Business Administration, Ankara 06800, Turkey) |
| Abstract: | This paper examines the predictive power of interest rate uncertainty over preprovision net revenues (PPNR) in a large panel of bank holding companies (BHC). Utilizing a linear dynamic panel model, we show that supplementing forecasting models with interest rate uncertainty improves the forecasting performance with the augmented model yielding lower forecast errors in comparison to a baseline model which includes unemployment rate, federal funds rate, and spread variables. Further separating PPNRs into two components that reflect net interest and noninterest income, we show that the predictive power of interest rate uncertainty is concentrated on the non-interest component of bank revenues. Finally, examining the point predictions under a severely stressed scenario, we show that the model can successfully predict the negative effect on overall bank revenues with a rise in the non-interest component of income during 2009:Q1. Overall, the findings suggest that stress testing exercises that involve bank revenue models can benefit from the inclusion of interest rate uncertainty and the cross-sectional information embedded in the panel of BHCs. |
| Keywords: | Bank stress tests; Empirical Bayes; Interest rate uncertainty; Out-of-sample forecasts |
| JEL: | C11 C14 C23 G21 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202040&r=all |
| By: | Darvas, Zsolt; Schepp, Zoltán |
| Abstract: | This paper presents unprecedented exchange rate forecasting results based upon a new model which approximates the gap between the fundamental equilibrium exchange rate and the actual exchange rate with the long-maturity forward exchange rate. The theoretical derivation of our forecasting equation is consistent with the monetary model of exchange rates. Our model outperforms the random walk in out-of-sample forecasting of twelve major currency pairs in both short and long horizons forecasts for the 1990-2020 period. The results are robust for all sub-periods with the exception of years around the collapse of Lehman Brothers in September 2008. Our results are robust to alternative model specifications, single equation and panel estimation, recursive and rolling estimation, and alternate data construction methods. The model performs better when the long-maturity forward exchange rate is assumed to be stationary as opposed to assuming non-stationarity. The improvement in forecast accuracy of our model is economically and statistically significant for almost all exchange rate series. The model is simple, linear, easy to replicate, and the data we use are available in real time and not subject to revisions. |
| Keywords: | exchange rate, error correction, forecasting performance, monetary model, out-of-sample, random walk |
| JEL: | F31 F37 |
| Date: | 2020–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:cvh:coecwp:2020/01&r=all |
| By: | Sium Bodha Hannadige; Jiti Gao; Mervyn J. Silvapulle; Param Silvapulle |
| Abstract: | This paper develops a method for forecasting a nonstationary time series, such as GDP, using a set of high-dimensional panel data as predictors. To this end, we use what is known as a factor augmented regression [FAR] model that contains a small number of estimated factors as predictors; the factors are estimated using time series data on a large number of potential predictors. The validity of this method for forecasting has been established when all the variables are stationary and also when they are all nonstationary, but not when they consist of a mixture of stationary and nonstationary ones. This paper fills this gap. More specifically, we develop a method for constructing an asymptotically valid prediction interval using the FAR model when the predictors include a mixture of stationary and nonstationary factors; we refer to this as mixture-FAR model. This topic is important because typically time series data on a large number of economic variables is likely to contain a mixture of stationary and nonstationary variables. In a simulation study, we observed that the mixture-FAR performed better than its competitor that requires all the variables to be nonstationary. As an empirical illustration, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production [IP], using the quarterly panel data on US macroeconomic variables, known as FRED-D. We observed that the mixture-FAR model proposed in this paper performed better than its aforementioned competitors. |
| Keywords: | bootstrap,generated factors, panel data, prediction interval. |
| JEL: | C22 C33 C38 C53 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-19&r=all |
| By: | Ilias Tsiakas (Department of Economics and Finance, University of Guelph, Canada; Rimini Centre for Economic Analysis); Jiahan Li (GMO LLC); Haibin Zhang (University of Guelph, Canada) |
| Abstract: | We detect cyclical variation in the predictive information of economic fundamentals, which can be used to substantially improve and simplify out-of-sample equity premium prediction. Economic fundamentals based on stock-specific information (notably the dividend yield) deliver better predictions in expansions. Economic fundamentals based on aggregate information (notably the short rate) deliver better predictions in recessions. Accordingly, a simple forecast combination of one predictor that generates cyclical forecasts and one predictor that generates countercyclical forecasts can deliver statistically significant and economically valuable equity premium predictions in both expansions and recessions. A prominent two-predictor forecast combination that performs well is the dividend yield and the short rate. Strategies designed for ex-ante timing of the business cycle can provide additional economic gains in equity premium prediction. |
| Keywords: | Equity Premium; Out-of-Sample Prediction; Economic Fundamentals; Business Cycle; Financial Cycle; Diversification |
| JEL: | G11 G14 G17 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:20-16&r=all |
| By: | Marcus Cobb; Jennifer Peña |
| Abstract: | This paper evaluates the performance of a suite of traditional models used to forecast short-term quarterly GDP, going from SARIMA, BVAR, dynamic factors, Bridge models to MIDAS. In total, 155 specifications are considered, and the accuracy of the forecasts is evaluated by means of a rolling out-of-sample prediction exercise for a four-quarter horizon. The main results suggest that forecasts improve as information from the current quarter is incorporated. Also that IMACEC is particularly useful given that it allows expressing GDP in monthly frequency. And finally, that relative performance of models can change abruptly with economic conditions, meaning that combinations of models tend to outperform most of the individual models, which is consistent with the literature. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:chb:bcchwp:871&r=all |
| By: | Natalia Bailey; Zvi Hochman; Yufeng Mao; Mervyn J. Silvapulle; Param Silvapulle |
| Abstract: | This paper introduces a statistical model to estimate and evaluate the predictability of the response of wheat yield to extreme temperature exposures and rainfall during the three phases of wheat grain production (vegetative, reproductive and grain filling) in northwestern (NW) Victoria, Australia. Unlike crop models which rely on functions developed from field experiments, we use observed data on annual wheat yields from 44 farms in the region over a period of 26 years (1993-2018). We find that the one-way fixed effects panel data model tends to outperform competing models in the out-of-sample prediction of future yields. We detect as positive drivers of NW Victorian wheat yield growth, exposure to moderate temperatures in all the three phases of the wheat production and total rainfall in the first two phases of the growing season. Providing adequate soil moisture, January-March rainfall also was found to be a positive driver of yields. Conversely, exposure to freezing temperatures during the vegetative and reproductive phases as well as to extreme high temperatures in all three phases of wheat production constitute negative drivers of NW Victorian wheat yields. The reproductive phase appears to be the most sensitive to climate variability, with adverse extreme heat and frost having sizeable negative impacts on yields. These negative effects are partially offset by increased rainfall in the same phase of wheat production. Moreover, we compare yield predictions by our statistical model to yield potentials calculated by APSIM. The gaps can be used to make recommendations on some adaptation opportunities available to farmers in the NW Victoria region. |
| Keywords: | extreme temperature exposure, crop yields, threshold-panel data model. |
| JEL: | C23 C53 Q54 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-18&r=all |
| By: | Christos Bouras (Department of Banking and Financial Management, University of Piraeus, 18534, Piraeus, Greece); Christina Christou (School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Keagile Lesame (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa) |
| Abstract: | We analyze the ability of an index of mortgage default risks (MDRI) for 43 states and 20 MSAs of the US derived from Google search queries, in predicting (in- and out-of-sample) housing returns of the corresponding states and MSAs, based on various panel data and time-series approaches. In general, our results tend to prefer the panel data model based on common correlated effects estimation. We highlight that growth in MDRI negatively impacts housing returns within-sample, with predictive gains primarily concentrated beyond a year. These results are robust to alternative out-of-sample periods and econometric frameworks. Given the role of house prices as a leading indicators, our results are of value to policymakers, especially at the longer-run. |
| Keywords: | Mortgage Default Risks, Housing Returns, States and MSAs, Panel Data Predictive Models |
| JEL: | C23 C53 R31 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202037&r=all |
| By: | Roman Frydman (New York University); Nicholas Mangee (Georgia Southern University); Josh Stillwagon (Babson College) |
| Abstract: | We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns. |
| Keywords: | stock-return forecasts, fundamentals, market sentiment, structural change, model ambiguity. |
| JEL: | G12 G14 C58 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:thk:wpaper:inetwp115&r=all |
| By: | Milan Fičura |
| Abstract: | Three different classes of data mining methods (k-Nearest Neighbour, Ridge Regression and Multilayer Perceptron Feed-Forward Neural Networks) are applied for the purpose of quantitative trading on 10 simulated time series, as well as real world time series of 10 currency exchange rates ranging from 1.11.1999 to 12.6.2015. Each method is tested in multiple variants. The k-NN algorithm is applied alternatively with the Euclidian, Manhattan, Mahalanobis and Maximum distance function. The Ridge Regression is applied as Linear and Quadratic, and the Feed-Forward Neural Network is applied with either 1, 2 or 3 hidden layers. In addition to that Principal Component Analysis (PCA) is eventually applied for the dimensionality reduction of the predictor set and the meta-parameters of the methods are optimized on the validation sample. In the simulation study a Stochastic-Volatility Jump-Diffusion model, extended alternatively with 10 different non-linear conditional mean patterns, is used, to simulate the asset price behaviour to which the tested methods are applied. The results show that no single method was able to profit on all of the non-linear patterns in the simulated time series, but instead different methods worked well for different patterns. Alternatively, past price movements and past returns were used as predictors. In the case when the past price movements were used, quadratic ridge regression achieved the most robust results, followed by some of the k-NN methods. In the case when past returns were used, k-NN based methods were the most consistently profitable, followed by the linear ridge regression and quadratic ridge regression. Neural networks, while being able to profit on some of the time series, did not achieve profit on most of the others. No evidence was further found of the PCA method to improve the results of the tested methods in a systematic way. In the second part of the study, the models were applied to empirical foreign exchange rate time series. Overall the profitability of the methods was rather low, with most of them ending with a loss on most of the currencies. The most profitable currency was EURUSD, followed by EURJPY, GBPJPY and EURGBP. The most successful methods were the linear ridge regression and the Manhattan distance based k-NN method which both ended with profits for most of the time series (unlike the other methods). Finally, a forward selection procedure using the linear ridge regression was applied to extend the original predictor set with some technical indicators. The selection procedure achieved limited success in improving the out-sample results for the linear ridge regression model but not the other models. |
| Keywords: | Ridge regression, k-Nearest Neighbour, Artificial Neural Networks, Principal Component Analysis, Exchange rate forecasting, Investment strategy, Market efficiency |
| JEL: | C45 C63 G11 G14 G17 |
| Date: | 2019–11–13 |
| URL: | http://d.repec.org/n?u=RePEc:prg:jnlwps:v:1:y:2019:id:1.001&r=all |
| By: | Fabrizio Cipollini; Giampiero M. Gallo; Alessandro Palandri |
| Abstract: | We build the time series of optimal realized portfolio weights from high-frequency data and we suggest a novel Dynamic Conditional Weights (DCW) model for their dynamics. DCW is benchmarked against popular model-based and model-free specifications in terms of weights forecasts and portfolio allocations. Next to portfolio variance, certainty equivalent and turnover, we introduce the break-even transaction costs as an additional measure that identifies the range of transaction costs for which one allocation is preferred to another. By comparing minimum-variance portfolios built on the components of the Dow Jones 30 Index, the proposed DCW overall attains the best allocations with respect to the measures considered, for any degree of risk-aversion, transaction costs and exposure. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.12400&r=all |
| By: | Arias-Calluari Karina; Alonso-Marroquin Fernando; Nattagh-Najafi Morteza; Harr\'e Michael |
| Abstract: | The outbreak of the novel coronavirus (COVID-19) has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history. However, in the past we have seen markets recover just as we will see the current markets recover again, so on this basis the recover of the markets will reach a minimum before increasing sometimes in the not-too-distant future. Here we present two forecast models of the S&P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA. The decline and recovery in the index is estimated for the following three months. The forecast is a projection of a prediction with uncertainties described by q-gaussian distributions. Our forecast was made on the premise that: (a) The prediction is based on deterministic trend of a data set since the viral spread of COVID-19 started , and (b) The uncertainties are fitted from patterns of the S\&P500 for the last 24 years. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.03969&r=all |
| By: | Franses, Ph.H.B.F. |
| Abstract: | This paper introduces a new autoregressive model, with the specific feature that the lag structure can vary over time. More precise, and to keep matters simple, the autoregressive model sometimes has lag 1, and sometimes lag 2. Representation, autocorrelation, specification, inference, and the creation of forecasts are presented. A detailed illustration for annual inflation rates for eight countries in Africa shows the empirical relevance of the new model. Various potential extensions are discussed. |
| Keywords: | Autoregression, Time-varying lags, Forecasting |
| JEL: | C22 C53 |
| Date: | 2020–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:126706&r=all |
| By: | Tao Hong; Pierre Pinson; Yi Wang; Rafal Weron; Dazhi Yang; Hamidreza Zareipour |
| Abstract: | Forecasting has been an essential part of the power and energy industry. Researchers and practitioners have contributed thousands of papers on forecasting electricity demand and prices, and renewable generation (e.g., wind and solar power). This paper offers a brief review of influential energy forecasting papers; summarizes research trends; discusses importance of reproducible research and points out six valuable open data sources; makes recommendations about publishing high-quality research papers; and offers an outlook into the future of energy forecasting. |
| Keywords: | Energy forecasting; Load forecasting; Electricity price forecasting; Wind forecasting; Solar forecasting |
| JEL: | C51 C52 C53 Q41 Q47 |
| Date: | 2020–05–07 |
| URL: | http://d.repec.org/n?u=RePEc:ahh:wpaper:worms2008&r=all |
| By: | Benjamin Jones |
| Abstract: | Robust forecasting of mining sector revenues is key to effective budgeting (and broader fiscal management) in many resource-rich countries. However, this is challenging in practice, given commodity market volatility, the extended lags (and often opaque processes) between resource discoveries and fiscal yields, and the heterogeneity of taxable entities within the sector. Such issues are exacerbated by capacity deficits: quantitative sector assessment frameworks are seldom employed or maintained by revenue authorities. |
| Keywords: | corporate income tax, Extractive industries, mineral tax, minerals, Mining, revenue forecasting, Royalties |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2020-22&r=all |
| By: | Payne, Jason Leslie (Australian National University); Morgan, Anthony |
| Abstract: | At the time of writing, there was 2.9 million confirmed cases of COVID-19 and more than 200,000 deaths worldwide. Not since the Spanish Flu in 1918 has the world experienced such a widespread pandemic and this has motivated many countries across globe to take a series of unprecedented actions in an effort to curb the spread and impact of the SARS-CoV-2 virus. Among these government and regulatory interventions includes unprecedented domestic and international travel restrictions as well as a raft of stay-at-home and social distancing regulations. Each has left criminologists wondering what impact this will have on crime in both the short- and long-term. In this study, we examine officially recorded violent crime rates for the month of March, 2020, as reported for the state of Queensland, Australia. We use ARIMA modeling techniques to compute six-month-ahead forecasts of common assault, serious assault, sexual offence and domestic violence order breach rates and then compare these forecasts (and their 95\% confidence intervals) with the observed data for March 2020. We conclude that the observed rates of reported violent offending across Queensland were not--at least not so far--significantly different from what was expected given the history of each offence series. |
| Date: | 2020–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:g4kh7&r=all |