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on Forecasting |
| By: | Knut Are Aastveit; Tuva Marie Fastbø; Eleonora Granziera; Kenneth Sæterhagen Paulsen; Kjersti Næss Torstensen |
| Abstract: | We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are free of sampling errors and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed-frequency data, we estimate various mixed-data sampling (MIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4-2020Q1. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high-frequency predictors. Finally, we illustrate the beneï¬ ts of using the card payments data by obtaining a timely and relatively accurate now cast of the ï¬ rst quarter of 2020, a quarter characterized by heightened uncertainty due to the COVID-19 pandemic. |
| Keywords: | debit card transaction data, nowcasting, forecast evaluation, COVID-19 |
| JEL: | C22 C52 C53 E27 |
| Date: | 2020–11–08 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2020_17&r= |
| By: | Afees A. Salisu (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility in a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective to market conditions as they directly relate to portfolio performance metrics from both a volatility and co-movement perspective and, unlike other macro-financial indicators of uncertainty or risk, can be integrated into diversification models within a forecasting and portfolio design setting. Since the two predictors are available at weekly frequency, and we want to provide forecast at the daily level, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models across both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared to the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also correlation patterns clearly play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes in that financial indicators that are directly associated with portfolio diversification performance metrics can also be utilized for forecasting purposes with significant implications for dynamic portfolio allocation strategies. |
| Keywords: | Systemic risk, Financial turbulence, Stock market, MIDAS models |
| JEL: | C32 D8 E32 G15 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202162&r= |
| By: | Simon Fritzsch; Maike Timphus; Gregor Weiss |
| Abstract: | Copulas. We study the model risk of multivariate risk models in a comprehensive empirical study on Copula-GARCH models used for forecasting Value-at-Risk and Expected Shortfall. To determine whether model risk inherent in the forecasting of portfolio risk is caused by the candidate marginal or copula models, we analyze different groups of models in which we fix either the marginals, the copula, or neither. Model risk is economically significant, is especially high during periods of crisis, and is almost completely due to the choice of the copula. We then propose the use of the model confidence set procedure to narrow down the set of available models and reduce model risk for Copula-GARCH risk models. Our proposed approach leads to a significant improvement in the mean absolute deviation of one day ahead forecasts by our various candidate risk models. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.10946&r= |
| By: | Blagov, Boris; Müller, Henrik; Jentsch, Carsten; Schmidt, Torsten |
| Abstract: | Corporate investment in Germany has been relatively weak for a prolonged period after the financial crisis. This was remarkable given that interest rates and overall economic activity, important determinants of corporate investment, developed quite favourably during that time. These developments highlight the fact that the dynamics of business cycles varies over time: each cycle is somewhat different. A promising new line of research to identify the driving factors of business cycles is the use of narratives (Shiller 2017, 2020). Widely shared stories capture expectations and beliefs about the workings of the economy that may influence economic behavior, such as investment decisions. In this paper, we use Latent Dirichlet Allocation (LDA) to identify topics from news (text) data related to corporate investment in Germany and to construct suitable indicators. Furthermore, we focus on isolating those investment narratives that show the potential to lead to substantial improvement of the forecasting performance of econometric models. In our analysis, we demonstrate the benefit of using media-based indicators to improve econometric forecasts of business equipment investment. Newspaper data carries important information both on the future developments of investment (forecasting) as well as on current developments (nowcasting). Moreover, the identified investment narrative enables the researcher to improve her/his understanding of the investment process in general and allows to incorporate exogenous developments as well as economic sentiment, news and other relevant events to the analysis. |
| Keywords: | narrative economics,mixed-frequency,nowcasting via media data |
| JEL: | C53 C82 E32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:921&r= |
| By: | Baumann, Ursel; Darracq Pariès, Matthieu; Westermann, Thomas; Riggi, Marianna; Bobeica, Elena; Meyler, Aidan; Böninghausen, Benjamin; Fritzer, Friedrich; Trezzi, Riccardo; Jonckheere, Jana; Kulikov, Dmitry; Popova, Dilyana; Pert, Sulev; Michail, Nektarios; Paloviita, Maritta; Brázdik, František; Pönkä, Harri; Bess, Mikkel; Vilmi, Lauri; Jørgensen, Casper; Robert, Pierre-Antoine; Al-Haschimi, Alexander; Gmehling, Philipp; Bańbura, Marta; Hartmann, Matthias; Charalampakis, Evangelos; Menz, Jan-Oliver; Hartwig, Benny; Schupp, Fabian; Hutchinson, John; Speck, Christian; Paredes, Joan; Volz, Ute; Reiche, Lovisa; Bragoudakis, Zacharias; Tirpák, Marcel; Kasimati, Evangelia; Tengely, Veronika; Łyziak, Tomasz; Tagliabracci, Alex; Stanisławska, Ewa; Bessonovs, Andrejs; Iskrev, Nikolay; Krasnopjorovs, Olegs; Gavura, Miroslav; Reichenbachas, Tomas; Damjanović, Milan; Colavecchio, Roberta; Maletic, Matjaz; Galati, Gabriele; Leiva, Danilo; Kearney, Ide; Stockhammar, Pär |
| Abstract: | This paper summarises the findings of the Eurosystem’s Expert Group on Inflation Expectations (EGIE), which was one of the 13 work streams conducting analysis that fed into the ECB’s monetary policy strategy review. The EGIE was tasked with (i) reviewing the nature and behaviour of inflation expectations, with a focus on the degree of anchoring, and (ii) exploring the role that measures of expectations can play in forecasting inflation. While it is households’ and firms’ inflation expectations that ultimately matter in the expectations channel, data limitations have meant that in practice the focus of analysis has been on surveys of professional forecasters and on market-based indicators. Regarding the anchoring of inflation expectations, this paper considers a number of metrics: the level of inflation expectations, the responsiveness of longer-term inflation expectations to shorter-term developments, and the degree of uncertainty. Different metrics can provide conflicting signals about the scale and timing of potential unanchoring, which underscores the importance of considering all of them. Overall, however, these metrics suggest that in the period since the global financial and European debt crises, longer-term inflation expectations in the euro area have become less well anchored. Regarding the role measures of inflation expectations can play in forecasting inflation, this paper finds that they are indicative for future inflationary developments. When it comes to their predictive power, both market-based and survey-based measures are found to be more accurate than statistical benchmarks, but do not systematically outperform each other. Beyond their role as standalone forecasts, inflation expectations bring forecast gains when included in forecasting models and can also inform scenario and risk analysis in projection exercises performed using structural models. ... JEL Classification: D84, E31, E37, E52 |
| Keywords: | anchoring, forecasting, Inflation expectations, macroeconomics, monetary policy |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbops:2021264&r= |
| By: | Alexander Jaax; Frédéric Gonzales; Annabelle Mourougane |
| Abstract: | The increasing importance of services trade in the global economy contrasts with the lack of timely data to monitor recent developments. The nowcasting models developed in this paper are aimed at providing insights into current changes in total services trade, as recorded in monthly statistics of the G7 countries. Combining machine-learning techniques and dynamic factor models, the methodology exploits traditional data and Google Trends search data. No single model outperforms the others, but a weighted average of the best models combining machine-learning with dynamic factor models seems to be a promising avenue. The best models improve one-step ahead predictive performance relative to a simple benchmark by 30-35% on average across G7 countries and trade flows. Nowcasting models are estimated to have captured about 67% of the fall in services exports due to the COVID-19 shock and 60% of the fall in imports on average across G7 economies. |
| Keywords: | Dynamic factor models, G7 economies, Machine learning |
| JEL: | C4 C22 F17 |
| Date: | 2021–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:oec:traaab:253-en&r= |