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on Econometric Time Series |
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Issue of 2025–09–29
six papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Todd Prono |
| Abstract: | In heavy-tailed cases, variance targeting the Student's-t estimator proposed in Bollerslev (1987) for the linear GARCH model is shown to be robust to density misspecification, just like the popular Quasi-Maximum Likelihood Estimator (QMLE). The resulting Variance-Targeted, Non-Gaussian, Quasi-Maximum Likelihood Estimator (VTNGQMLE) is shown to possess a stable limit, albeit one that is highly non-Gaussian, with an ill-defined variance. The rate of convergence to this non-standard limit is slow relative √n and dependent upon unknown parameters. Fortunately, the sub-sample bootstrap is applicable, given a carefully constructed normalization. Surprisingly, both Monte Carlo experiments and empirical applications reveal VTNGQMLE to sizably outperform QMLE and other performance-enhancing (relative to QMLE) alternatives. In an empirical application, VTNGQMLE is applied to VIX (option-implied volatility of the S&P 500 Index). The resulting GARCH variance estimates are then used to forecast option-implied volatility of volatility (VVIX), thus demonstrating a link between historical volatility of VIX and risk-neutral volatility-of-volatility. |
| Keywords: | GARCH; VIX; VVIX; Heavy tails; Robust estimation; Variance forecasting; Volatility; Volatility-of-volatility |
| JEL: | C13 C22 C58 |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-75 |
| By: | Degui Li (University of Macau); Yuning Li (University of York); Peter C.B. Phillips (Yale University) |
| Abstract: | This paper studies high-dimensional curve time series with common stochastic trends. A dual functional factor model structure is adopted with a high-dimensional factor model for the observed curve time series and a low-dimensional factor model for the latent curves with common trends. A functional PCA technique is applied to estimate the common stochastic trends and functional factor loadings. Under some regularity conditions we derive the mean square convergence and limit distribution theory for the developed estimates, allowing the dimension and sample size to jointly diverge to infinity. We propose an easy-to-implement criterion to consistently select the number of common stochastic trends and further discuss model estimation when the nonstationary factors are cointegrated. Extensive Monte-Carlo simulations and two empirical applications to large-scale temperature curves in Australia and log-price curves of S&P 500 stocks are conducted, showing finite-sample performance and providing practical implementations of the new methodology. |
| Date: | 2025–09–15 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2460 |
| By: | Stéphane Lhuissier |
| Abstract: | I propose a dynamic factor model with time-varying skewness to assess asymmetric risk around the economic outlook across a set of macroeconomic aggregates. Applied to U.S. data, the model shows that macroeconomic skewness is procyclical, displays significant independent variations from GDP growth skewness, and does not require conditioning on financial variables to manifest. Compared to univariate benchmarks, the model improves the detection of downside risk to growth and delivers more accurate predictive distributions, especially during downturns. These findings underscore the value of using a richer information set to quantify the balance of macroeconomic risks. |
| Keywords: | Dynamic Factor Models, Markov-Switching, Skewness |
| JEL: | C34 C38 C53 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:1004 |
| By: | Wenze Li |
| Abstract: | We propose a simple modification to the wild bootstrap procedure and establish its asymptotic validity for linear regression models with many covariates and heteroskedastic errors. Monte Carlo simulations show that the modified wild bootstrap has excellent finite sample performance compared with alternative methods that are based on standard normal critical values, especially when the sample size is small and/or the number of controls is of the same order of magnitude as the sample size. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.20972 |
| By: | Anas Abdelhakmi; Andrew E. B. Lim |
| Abstract: | Prediction models calibrated using historical data may forecast poorly if the dynamics of the present and future differ from observations in the past. For this reason, predictions can be improved if information like forward looking views about the state of the system are used to refine the forecast. We develop an approach for combining a dynamic factor model for risky asset prices calibrated on historical data, and noisy expert views of future values of the factors/covariates in the model, and study the implications for dynamic portfolio choice. By exploiting the graphical structure linking factors, asset prices, and views, we derive closed-form expressions for the dynamics of the factor and price processes after conditioning on the views. For linear factor models, the price process becomes a time-inhomogeneous affine process with a new covariate formed from the views. We establish a novel theoretical connection between the conditional factor process and a process we call a Mean-Reverting Bridge (MrB), an extension of the classical Brownian bridge. We derive the investor's optimal portfolio strategy and show that views influence both the myopic mean-variance term and the intertemporal hedge. The optimal dynamic portfolio when the long-run mean of the expected return is uncertain and learned online from data is also derived. More generally, our framework offers a generalizable approach for embedding forward-looking information about covariates in a dynamic factor model. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11528 |
| By: | Andr\'es L. Su\'arez-Cetrulo; Alejandro Cervantes; David Quintana |
| Abstract: | Financial markets are complex, non-stationary systems where the underlying data distributions can shift over time, a phenomenon known as regime changes, as well as concept drift in the machine learning literature. These shifts, often triggered by major economic events, pose a significant challenge for traditional statistical and machine learning models. A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data, making it difficult to evaluate a model's ability to detect and recover from these drifts. This paper addresses this challenge by introducing a novel framework, named ProteuS, for generating semi-synthetic financial time series with pre-defined structural breaks. Our methodology involves fitting ARMA-GARCH models to real-world ETF data to capture distinct market regimes, and then simulating realistic, gradual, and abrupt transitions between them. The resulting datasets, which include a comprehensive set of technical indicators, provide a controlled environment with a known ground truth of regime changes. An analysis of the generated data confirms the complexity of the task, revealing significant overlap between the different market states. We aim to provide the research community with a tool for the rigorous evaluation of concept drift detection and adaptation mechanisms, paving the way for more robust financial forecasting models. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11844 |