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on Econometric Time Series |
| By: | TAYANAGI, Toshikazu; 田柳, 俊和; KUROZUMI, Eiji; 黒住, 英司 |
| Keywords: | Structural break, structural change, break point, break fraction |
| JEL: | C13 C22 |
| Date: | 2023–11–14 |
| URL: | http://d.repec.org/n?u=RePEc:hit:econdp:2023-04&r=ets |
| By: | Joao Vitor Matos Goncalves; Michel Alexandre; Gilberto Tadeu Lima |
| Abstract: | This paper assesses the impact of time horizon on the relative performance of traditional econometric models and machine learning models in forecasting stock market prices. We employ an extensive daily series of Brazil IBX50 closing prices between 2012 and 2022 to compare the performance of two forecasting models: ARIMA (autoregressive integrated moving average) and LSTM (long short-term memory) models. Our results suggest that the ARIMA model predicts better data points that are closer to the training data, as it loses predictive power as the forecast window increases. We also find that the LSTM model is a more reliable source of prediction when dealing with longer forecast windows, yielding good results in all the windows tested in this paper. |
| Keywords: | Finance; machine learning; deep learning; stock market |
| JEL: | C22 C45 C53 G17 |
| Date: | 2023–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2023wpecon13&r=ets |
| By: | Verena Monschang; Mark Trede; Bernd Wilfling |
| Abstract: | Quaedvlieg (2021, Journal of Business & Economic Statistics) proposes a uniform Superior Predictive Ability (uSPA) test for comparing forecasts across multiple horizons. The procedure is based on a 'minimum Diebold-Mariano' test statistic, and asymptotic critical values are obtained via bootstrapping. We show, theoretically and via simulations, that Quaedvlieg's test is subject to massive size distortions. In this article, we establish several convergence results for the 'minimum Diebold- Mariano' statistic, revealing that appropriate asymptotic critical values are given by the quantiles of the standard normal distribution. The uSPA test modified this way (i) always keeps the nominal size, (ii) is size-exploiting along the boundary that separates the parameter subsets of the null and the alternative uSPA hypotheses, and (iii) is consistent. Based on the closed skew normal distribution, we present a procedure for approximating the power function and demonstrate the favorable finite-sample properties of our test. In an empirical replication, we find that Quaedvlieg's (2021) results on uSPA comparisons between direct and iterative forecasting methods are statistically inconclusive. |
| Keywords: | Forecast evaluation, Joint-hypothesis testing, Stochastic dominance, Closed skew normal distribution |
| JEL: | C12 C15 C52 C53 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:10623&r=ets |
| By: | Philippe Goulet Coulombe (University of Quebec in Montreal); Mikael Frenette (University of Quebec in Montreal); Karin Klieber (Oesterreichische Nationalbank) |
| Abstract: | We reinvigorate maximum likelihoode stimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network which accommodates for various forms of time variation in the error variance. Second, we introducea volatility emphasis constraint that breaks mean/variance indeterminacy in this class of overparametrized nonlinear models. Third, we conduct a blocked out-of-bag reality check to curb overfitting in both conditional moments.Fourth, the algorithm utilizes standard deep learning software and thus handles large datasets – both computationally and statistically. Ergo, our Hemisphere Neural Network (HNN) provides proactive volatility forecasts based on leading indicators when it can, and reactive volatility based on the magnitude of previous prediction errors when it must. We evaluate point and density forecasts with an extensive out-of-sample experiment and benchmark against a suite of models ranging from classics to more modern machine learning-based offerings. In all cases, HNN fares well by consistently providing accurate mean/variance forecasts for all targets and horizons. Studying the resulting volatility paths reveals its versatility, while probabilistic forecasting evaluation metrics showcase its enviable reliability. Finally, we also demonstrate how this machinery can be merged with other structured deep learning models by revisiting Goulet Coulombe(2022)’s Neural Phillips Curve. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:bbh:wpaper:23-04&r=ets |
| By: | David T. Frazier; Ryan Covey; Gael M. Martin; Donald S. Poskitt |
| Abstract: | The forecast combination puzzle is the commonly encountered empirical result whereby predictions formed by combining multiple forecasts in complex ways do not out-perform more naive, e.g. equally-weighted, approaches. While various solutions for the cause of the puzzle exist in the literature, these solutions are limited in their scope and applicability. In contrast, we demonstrate a general solution to the puzzle by showing that this phenomenon is a direct consequence of the methodology used to produce forecast combinations. In particular, we show that tests which aim to discriminate between the predictive accuracy of competing forecast combination strategies have low power, and can lack size control, leading to an outcome that favours the naive approach. In addition, we demonstrate that the low power of such predictive accuracy tests in the forecast combination setting can be completely avoided if more efficient strategies are used in the production of the combinations. We illustrate these findings both in the context of forecasting a functional of interest and in terms of predictive densities. A short empirical example using daily financial returns exemplifies how researchers can avoid the puzzle in practical settings. |
| Keywords: | optimal forecast combinations, tests for forecast accuracy, probabilistic forecasting, scoring rules, S&P500 forecasting, one-step versus two-step estimation |
| JEL: | C18 C12 C53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2023-18&r=ets |
| By: | Felix Chan; Laurent Pauwels |
| Abstract: | The optimal aggregation of forecasts produced either from models or expert judgements presents an interesting challenge for managerial decisions. Mean absolute error (MAE) and mean squared error (MSE) losses are commonly employed as criteria of optimality to obtain the weights that combine multiple forecasts. While much is known about MSE in the context of forecast combination, less attention has been given to MAE. This paper shows that the optimal solutions from minimizing either MAE or MSE loss functions, i.e., the optimal weights, are equivalent provided that the weights sum to one. The equivalence holds under mild assumptions and includes a wide class of symmetric and asymmetric error distributions. The theoretical results are supported by a numerical study that features skewed and fat-tailed distributions. The practical implications of combining forecasts with MAE and MSE optimal weights are investigated empirically with a small sample of data on expert forecasts on inflation, growth, and unemployment rates for the European Union. The results show that MAE weights are less sensitive to outliers, and MSE and MAE weights can be close to equivalent even when the sample is small. |
| Keywords: | forecasting, forecast combination, optimization, mean absolute error, optimal weights |
| JEL: | C53 C61 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2023-59&r=ets |
| By: | Carlo A. Favero; Ruben Fernandez-Fuertes |
| Abstract: | This paper proposes an Affine Macro Term Structure model in which yields are drifting, sharing a common stochastic trend driven by the drift in short-term (monetary policy) rates and excess returns are stationary as the compensation for risk is driven by the cycles in yields. We apply the approach to US data and compare the empirical results from the new specification with those obtained from standard Affine Term Structure models. The cycle-trend decompositionbased Affine Term Structure model produces much better forecasts of the dynamics of yields and, consequently, different and stationary dynamics for the term premia. |
| Keywords: | Affine Term Structure Models, Trends and Cycles, Term Premia |
| JEL: | E43 E52 G12 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp23210&r=ets |