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on Econometric Time Series |
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Issue of 2026–04–13
eleven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Daichi Hiraki; Siddhartha Chib; Yasuhiro Omori |
| Abstract: | We develop a dynamic factor stochastic volatility-in-mean (SVM) specification for vector autoregressions (VARs) that embeds an SVM component within a dynamic factor stochastic volatility structure. A small number of latent volatility factors capture common movements in conditional variances, while volatility enters the conditional mean of the VAR. This specification allows time-varying uncertainty to influence macroeconomic dynamics through both second moments and expected outcomes while preserving tractability in large panels. We construct an efficient Markov chain Monte Carlo algorithm for estimation in this high-dimensional, non-Gaussian setting. Using quarterly data on twenty variables from the FRED-QD database, we compare predictive performance with the benchmark stochastic volatility VAR model. The dynamic factor SVM specification delivers superior forecasts for more variables during major macroeconomic disruptions such as the 2008 global financial crisis. The results indicate that allowing volatility to enter the mean captures an important transmission channel in macroeconomic dynamics. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.04529 |
| By: | Alexandre Alouadi; Gr\'egoire Loeper; C\'elian Marsala; Othmane Mazhar; Huy\^en Pham |
| Abstract: | We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existing approaches typically fail to jointly model drift and stochastic volatility, as diffusion-based methods fix the volatility while martingale transport models ignore drift. We introduce the Schr\"odinger-Bass Bridge for Time Series (SBBTS), a unified framework that extends the Schr\"odinger-Bass formulation to multi-step time series. The method constructs a diffusion process that jointly calibrates drift and volatility and admits a tractable decomposition into conditional transport problems, enabling efficient learning. Numerical experiments on the Heston model demonstrate that SBBTS accurately recovers stochastic volatility and correlation parameters that prior Schr\"odingerBridge methods fail to capture. Applied to S&P 500 data, SBBTS-generated synthetic time series consistently improve downstream forecasting performance when used for data augmentation, yielding higher classification accuracy and Sharpe ratio compared to real-data-only training. These results show that SBBTS provides a practical and effective framework for realistic time series generation and data augmentation in financial applications. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.07159 |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Wei Long (Department of Economics, Tulane University, New Orleans, LA 70118, USA) |
| Abstract: | Quantile regression is widely used to study heterogeneous effects, but inference in time series settings remains challenging when regression errors are serially correlated. Building on the dependent wild bootstrap, we develop an inference procedure for linear time series quantile regression that reweights the restricted quantile score with tapered multipliers and employs a one-step bootstrap update together with the HAC-based studentization. The procedure avoids repeated solution of a non-smooth quantile regression problem within each bootstrap draw while targeting the same inferential object as robust HAC testing. Under strong mixing and standard smoothness and bandwidth conditions, we establish asymptotic validity of the bootstrap test and derive its local power under Pitman alternatives. Monte Carlo results indicate improved size control relative to conventional and robust HAC methods, especially under strong dependence, with only modest differences in power. An application to the determinants of U.S. housing prices over the past four decades illustrates the practical usefulness of the method. |
| Keywords: | Time series quantile regression; Dependent wild bootstrap; HAC inference |
| JEL: | C12 C22 C46 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202612 |
| By: | Zheqi Fan (Melody); Meng (Melody); Wang; Yifan Ye |
| Abstract: | We examine whether model-based spot volatility estimators extracted from traded options data enhance the predictive power of the Heterogeneous Autoregressive (HAR) model for realized volatility. Specifically, we infer spot volatility under the rough stochastic volatility model via an iterative two-step approach following Andersen et al. (2015a) and adopt a deep learning surrogate to accelerate model estimation from large-scale options panels. Benchmarked against traditional stochastic volatility models (Heston, Bates, SVCJ) and the VIX index, our results demonstrate that the augmented HAR-RV-RHeston model improves daily realized volatility forecasting accuracy and sustains superior performance across horizons up to one month. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02743 |
| By: | James A. Duffy; Sophocles Mavroeidis |
| Abstract: | We study identification in structural vector autoregressions (SVARs) in which the endogenous variables enter nonlinearly on the left-hand side of the model, a feature we term endogenous nonlinearity, to distinguish it from the more familiar case in which nonlinearity arises only through exogenous or predetermined variables. This class of models accommodates asymmetric impact multipliers, endogenous regime switching, and occasionally binding constraints. We show that, under weak regularity conditions, the model parameters and structural shocks are (nonparametrically) identified up to an orthogonal transformation, exactly as in a linear SVAR. Our results have the powerful implication that most existing identification schemes for linear SVARs extend directly to our nonlinear setting, with the number of restrictions required to achieve exact identification remaining unchanged. We specialise our results to piecewise affine SVARs, which provide a convenient framework for the modelling of endogenous regime switching, and their smooth transition counterparts. We illustrate our methodology with an application to the nonlinear Phillips curve, providing a test for the presence of nonlinearity that is robust to the choice of identifying assumptions, and finding significant evidence for state-dependent inflation dynamics. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.07718 |
| By: | Dario Caldara; Haroon Mumtaz; Molin Zhong |
| Abstract: | We characterize asymmetric tail risk across over one hundred U.S. macroeconomic and financial variables using a dynamic factor model with stochastic volatility. The model unifies growth-at-risk, inflation-at-risk, and sectoral heterogeneity through common factors whose volatility responds endogenously to shocks, combined with heterogeneous factor loadings. We find that asymmetric tail risk is pervasive and heterogeneous: some sectors exhibit severe asymmetry while others show minimal asymmetry, with variation across activity, price, and financial variables. The framework disentangles supply- and demand-driven tail risk dynamics, revealing how the balance of risks shifts across episodes, and identifies where vulnerabilities concentrate across the economy. |
| Keywords: | Business fluctuations and cycles; Econometric modeling; Risk analysis; Volatility |
| JEL: | C11 C32 C38 E32 E44 |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgif:102988 |
| By: | Tom\'as del Barrio Castro; Alain Hecq; Sean Telg |
| Abstract: | This paper investigates the role of complex and negative roots in mixed causal-noncausal autoregressive (MAR) models. Using partial fraction decompositions, we show that seasonal roots can always be isolated in the moving average representation of purely causal and noncausal AR models. We find that this result extends to the MAR model, which means that no new joint seasonal effects can be generated despite the multiplicative structure of the causal and noncausal polynomials. This results has important consequences for the MAR model selection procedure and these are extensively studied in a Monte Carlo simulation study. An empirical application on COVID-19 and soybean data illustrates the main findings of the paper. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.07040 |
| By: | Diego Vallarino |
| Abstract: | We study identification and inference in nonlinear dynamic systems defined on unknown interaction networks. The system evolves through an unobserved dependence matrix governing cross-sectional shock propagation via a nonlinear operator. We show that the network structure is not generically identified, and that identification requires sufficient spectral heterogeneity. In particular, identification arises when the network induces non-exchangeable covariance patterns through heterogeneous amplification of eigenmodes. When the spectrum is concentrated, dependence becomes observationally equivalent to common shocks or scalar heterogeneity, leading to non-identification. We provide necessary and sufficient conditions for identification, characterize observational equivalence classes, and propose a semiparametric estimator with asymptotic theory. We also develop tests for network dependence whose power depends on spectral properties of the interaction matrix. The results apply to a broad class of economic models, including production networks, contagion models, and dynamic interaction systems. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.04961 |
| By: | Haroon Mumtaz; Sofia Velasco |
| Abstract: | This paper develops a dynamic factor model in which common level and volatility factors evolve jointly, allowing conditional means and variances to interact endogenously within a large-information setting. The joint evolution of these factors provides a tractable framework for modeling risk, as fluctuations in volatility affect both the dispersion and the location of outcomes, generating state-dependent and asymmetric tail risks in predictive distributions. Volatility is captured by latent common factors that drive co-movement in second moments across a large panel, while heavy-tailed idiosyncratic shocks absorb transitory outliers and isolate persistent uncertainty dynamics. The framework embeds these interactions directly within a factor structure, allowing risk to arise endogenously from the joint dynamics of the system rather than being imposed through reduced-form approaches. Empirically, the model delivers systematic improvements in density forecast accuracy, particularly in the tails of the predictive distribution and at medium horizons. An application to international inflation highlights a dominant global level component in advanced economies and stronger regional and volatility contributions in emerging and developing economies, pointing to substantial heterogeneity in the role of uncertainty across countries. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.03681 |
| By: | Karmanpartap Singh Sidhu; Pranshi Saxena |
| Abstract: | This research addresses accurate option pricing by employing models beyond the traditional Black-Scholes framework. While Black-Scholes provides a closed-form solution, it is limited by assumptions of constant volatility, no dividends, and continuous price movements. To overcome these limitations, we use Monte Carlo simulation alongside the GARCH model, Heston stochastic volatility model, and Merton jump-diffusion model. The Black-Scholes-Monte Carlo method simulates diverse stock price paths using geometric Brownian motion. The GARCH model forecasts time-varying volatility from historical data. The Heston model incorporates stochastic volatility to capture volatility clustering and skew. The Merton jump-diffusion model adds sudden price jumps via a Poisson process. Results show the Heston model consistently produces estimates closer to market prices, while the Merton model performs well for volatile assets with sudden price movements. The GARCH model provides improved volatility forecasts for future option price prediction. All experiments used live market data from November 2024. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.06068 |
| By: | Marc Burri; Daniel Kaufmann |
| Abstract: | We extend the heteroskedasticity IV estimator of Rigobon and Sack (2004) from one to multiple monetary policy shocks by imposing recursive zero restrictions on the impact matrix. Unlike high-frequency identification, the approach requires neither intraday tick data nor precise announcement timestamps, making it applicable to countries or historical periods where such data are unavailable. Applied to US FOMC announcements, we find causal effects similar to those of high-frequency identification. The heteroskedasticity-based instrument passes weak-instrument tests for the target shock, whereas high-frequency surprises fail. For the path shock, we also find strong heteroskedasticity-based instruments in key specifications, and we show that the underlying shocks are similar to those based on high-frequency identification. |
| Keywords: | Monetary policy shocks, causal effects, forward guidance, heteroskedasticity, high-frequency, instrumental variables |
| JEL: | C3 E3 E4 E5 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:irn:wpaper:26-06 |