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on Forecasting |
| By: | Bucci, Andrea |
| Abstract: | Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The Cholesky-Artificial Neural Networks specification here presented provides a twofold advantage for this topic. On the one hand, the use of the Cholesky decomposition ensures positive definite forecasts. On the other hand, the implementation of artificial neural networks allows to specify nonlinear relations without any particular distributional assumption. Out-of-sample comparisons reveal that Artificial neural networks are not able to strongly outperform the competing models. However, long-memory detecting networks, like Nonlinear Autoregressive model process with eXogenous input and long shortterm memory, show improved forecast accuracy respect to existing econometric models. |
| Keywords: | Neural Networks; Machine Learning; Stock market volatility; Realized Volatility |
| JEL: | C22 C45 C53 G17 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95137&r=all |
| By: | Michael Cai (Northwestern University); Marco Del Negro (FRB New York); Edward Herbst (Federal Reserve Board); Ethan Matlin (FRB New York); Reca Sarfati (FRB New York); Frank Schorfheide (Department of Economics, University of Pennsylvania) |
| Abstract: | This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for \online" estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts of macroeconomic variables and interest rate expectations. We also study whether the predictive ability of DSGE models changes when we use priors that are substantially looser than those that are commonly adopted in the literature. |
| Keywords: | Adaptive algorithms, Bayesian inference, density forecasts, online estimation, sequential Monte Carlo methods |
| JEL: | C11 C32 C53 E32 E37 E52 |
| Date: | 2019–07–22 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:19-014&r=all |
| By: | Oguzhan Cepni (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050 Ulus, Altndag, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); I. Ethem Guney (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050 Ulus, Altndag, Ankara, Turkey); M. Hasan Yilmaz (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050 Ulus, Altndag, Ankara, Turkey) |
| Abstract: | In this paper, we forecast local currency debt of five major emerging market countries (Brazil, Indonesia, Mexico, South Africa, and Turkey) over the period of January 2010 to January 2019 (with an in-sample: March 2005 to December 2018). We exploit information from a large set of economic and financial time series to assess the importance of not only “own-country” factors (derived from principal component and partial least squares approach), but also create “global” predictors by combining the country-specific variables across the five emerging economies. We find that while information on own-country factors can outperform the historical average model, global factors tend to produce not only greater statistical and economic gains, but also enhances market timing ability of investors, especially when we use the target-variable (bond premium) approach under the partial least squares method to extract our factors. Our results have important implications for not only fund managers, but also policymakers. |
| Keywords: | Bond risk premia, Emerging markets, Factor extraction methods, Out-of-sample forecasting |
| JEL: | C22 C53 G12 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201957&r=all |
| By: | Brummelhuis, Raymond; Luo, Zhongmin |
| Abstract: | The 2007-09 financial crisis revealed that the investors in the financial market were more concerned about the future as opposed to the current capital adequacy for banks. Stress testing promises to complement the regulatory capital adequacy regimes, which assess a bank's current capital adequacy, with the ability to assess its future capital adequacy based on the projected asset-losses and incomes from the forecasting models from regulators and banks. The effectiveness of stress-test rests on its ability to inform the financial market, which depends on whether or not the market has confidence in the model-projected asset-losses and incomes for banks. Post-crisis studies found that the stress-test results are uninformative and receive insignificant market reactions; others question its validity on the grounds of the poor forecast accuracy using linear regression models which forecast the banking-industry incomes measured by Aggregate Net Interest Margin. Instead, our study focuses on NIM forecasting at an individual bank's level and employs both linear regression and non-linear Machine Learning techniques. First, we present both the linear and non-linear Machine Learning regression techniques used in our study. Then, based on out-of-sample tests and literature-recommended forecasting techniques, we compare the NIM forecast accuracy by 162 models based on 11 different regression techniques, finding that some Machine Learning techniques as well as some linear ones can achieve significantly higher accuracy than the random-walk benchmark, which invalidates the grounds used by the literature to challenge the validity of stress-test. Last, our results from forecast accuracy comparisons are either consistent with or complement those from existing forecasting literature. We believe that the paper is the first systematic study on forecasting bank-specific NIM by Machine Learning Techniques; also, it is a first systematic study on forecast accuracy comparison including both linear and non-linear Machine Learning techniques using financial data for a critical real-world problem; it is a multi-step forecasting example involving iterative forecasting, rolling-origins, recalibration with forecast accuracy measure being scale-independent; robust regression proved to be beneficial for forecasting in presence of outliers. It concludes with policy suggestions and future research directions. |
| Keywords: | Regression, Machine Learning, Time Series Analysis, Bank Capital, Stress Test, Net Interest Margin, Forecasting, PPNR, CCAR |
| JEL: | C4 C45 C5 C58 C6 G01 |
| Date: | 2019–03–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:94779&r=all |
| By: | Eliasson, Jonas |
| Abstract: | This paper compares the performance of several models forecasting travel time variability for road traffic, using before/after data from the introduction of the Stockholm congestion charges. Models are estimated on before-data, and the models’ forecasts for the after-situation are compared to actual after measurements. The accuracy of the models vary substantially, but several models are able to forecast the benefits from reduced travel time variability with sufficient accuracy to make them useful for decision making. |
| Keywords: | Travel time variability; reliability; cost benefit analysis; congestion pricing |
| JEL: | R41 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:94817&r=all |
| By: | Katarzyna Maciejowska; Rafal Weron |
| Abstract: | Electricity price forecasting (EPF) is an actively developing research field, which aims at predicting the spot and forward prices in wholesale electricity markets. Since day-ahead forecasting has gained the most attention, in this article we review the modeling approaches for short-term predictions, with a particular focus on variable selection. |
| Keywords: | Forecasting; Electricity Spot Price; Day-ahead Market; Variable Selection; Regularization; Regression; Quantile Regression |
| JEL: | C22 C32 C45 C51 C53 C70 L11 Q41 Q47 |
| Date: | 2019–02–07 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1901&r=all |