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on Econometric Time Series |
| By: | D’Innocenzo, Enzo (University of Bologna); Lucas, André (Vrije Universiteit Amsterdam and Tinbergen Institute); Schwaab, Bernd (European Central Bank); Zhang, Xin (Research Department, Central Bank of Sweden) |
| Abstract: | We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for peaks-over-threshold (POT) dynamics. Unlike earlier approaches, our model (i) has unit root-like, i.e., integrated autoregressive dynamics for the GPD tail shape, and (ii) re-scales POTs by their thresholds to obtain a more parsimonious model with only one time-varying parameter to describe the entire tail. We establish parameter regions for stationarity, ergodicity, and invertibility for the integrated time-varying parameter model and its filter, and formulate conditions for consistency and asymptotic normality of the maximum likelihood estimator. Using four exchange rate series, we illustrate how the new model captures the dynamics of extreme VaR and ES. |
| Keywords: | dynamic tail risk; integrated score-driven models; extreme value theory |
| JEL: | C22 G11 |
| Date: | 2025–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:rbnkwp:0446 |
| By: | João A. Bastos |
| Abstract: | A deep learning binary classifier is proposed to test if asset returns follow martingale difference sequences. The Neyman-Pearson classification paradigm is applied to control the type I error of the test. In Monte Carlo simulations, I find that this approach has better power properties than variance ratio and portmanteau tests against several alternative processes. I apply this procedure to a large set of exchange rate returns and find that it detects several potential deviations from the martingale difference hypothesis that the conventional statistical tests fail to capture. |
| Keywords: | Martingale difference hypothesis; Convolutional network; Variance ratio test; Portmanteau test; Exchange rates. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03742025 |
| By: | James Mitchell; Taylor Shiroff |
| Abstract: | No, first estimates of state GDP growth are not rational forecasts, except for Georgia. Revisions to first estimates of state-level GDP growth tend to be biased, large, and/or predictable using information known at the time of the first estimate. |
| Keywords: | data revisions; real-time data; state GDP; forecast efficiency |
| JEL: | E01 R11 |
| Date: | 2025–04–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedcwq:99878 |
| By: | Jongrim Ha; M. Ayhan Kose; Christopher Otrok; Eswar S. Prasad |
| Abstract: | We develop a new dynamic factor model to jointly characterize global macroeconomic and financial cycles and the spillovers between them. The model decomposes macroeconomic cycles into the part driven by global and country-specific macro factors and the part driven by spillovers from financial variables. We consider cycles in macroeconomic aggregates (output, consumption and investment) and financial variables (equity and house prices and interest rates). The global macro factor plays a major role in explaining G-7 business cycles, but there are also sizeable spillovers from equity and house price shocks onto macroeconomic aggregates, at least over the past two decades, accounting for up to 20 percent of the variation in global business cycle fluctuations. These spillovers operate mainly through the global macro factor rather than the country-specific macro factors (i.e., these spillovers affect business cycles in all G-7 economies) and are stronger in the period leading up to and following the global financial crisis. We find weaker evidence of spillovers from macroeconomic cycles to financial variables, perhaps reflecting the predictive power of global financial markets. |
| Keywords: | global business cycles; global financial cycles; common shocks; international spillovers; dynamic factor models |
| JEL: | E32 F4 C32 C1 |
| Date: | 2025–04–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:99897 |