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on Econometric Time Series |
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Issue of 2025–09–15
twelve papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Harrison Katz; Robert E. Weiss |
| Abstract: | High-dimensional vector autoregressive (VAR) models offer a versatile framework for multivariate time series analysis, yet face critical challenges from over-parameterization and uncertain lag order. In this paper, we systematically compare three Bayesian shrinkage priors (horseshoe, lasso, and normal) and two frequentist regularization approaches (ridge and nonparametric shrinkage) under three carefully crafted simulation scenarios. These scenarios encompass (i) overfitting in a low-dimensional setting, (ii) sparse high-dimensional processes, and (iii) a combined scenario where both large dimension and overfitting complicate inference. We evaluate each method in quality of parameter estimation (root mean squared error, coverage, and interval length) and out-of-sample forecasting (one-step-ahead forecast RMSE). Our findings show that local-global Bayesian methods, particularly the horseshoe, dominate in maintaining accurate coverage and minimizing parameter error, even when the model is heavily over-parameterized. Frequentist ridge often yields competitive point forecasts but underestimates uncertainty, leading to sub-nominal coverage. A real-data application using macroeconomic variables from Canada illustrates how these methods perform in practice, reinforcing the advantages of local-global priors in stabilizing inference when dimension or lag order is inflated. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.05489 |
| By: | Todd Clark; Florian Huber; Gary Koop |
| Abstract: | This paper proposes a Vector Autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically lessens the risk of mis-specification. Second, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonlinearities across variables. Third, Bayesian computation using MCMC is straightforward even in very high dimensional models, allowing for efficient, equation by equation estimation, thus avoiding computational bottlenecks that arise in popular alternatives such as the time varying parameter VAR. Fourth, existing methods for identifying structural economic shocks in linear factor models can be adapted for the nonlinear case in a straightforward fashion using our model. Exercises involving artificial and macroeconomic data illustrate the properties of our model and its usefulness for forecasting and structural economic analysis. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.13972 |
| By: | Yanlong Wang; Jian Xu; Fei Ma; Hongkang Zhang; Hang Yu; Tiantian Gao; Yu Wang; Haochen You; Shao-Lun Huang; Danny Dongning Sun; Xiao-Ping Zhang |
| Abstract: | Financial time series forecasting is both highly significant and challenging. Previous approaches typically standardized time series data before feeding it into forecasting models, but this encoding process inherently leads to a loss of important information. Moreover, past time series models generally require fixed numbers of variables or lookback window lengths, which further limits the scalability of time series forecasting. Besides, the interpretability and the uncertainty in forecasting remain areas requiring further research, as these factors directly impact the reliability and practical value of predictions. To address these issues, we first construct a diverse financial image-text dataset (FVLDB) and develop the Uncertainty-adjusted Group Relative Policy Optimization (UARPO) method to enable the model not only output predictions but also analyze the uncertainty of those predictions. We then proposed FinZero, a multimodal pre-trained model finetuned by UARPO to perform reasoning, prediction, and analytical understanding on the FVLDB financial time series. Extensive experiments validate that FinZero exhibits strong adaptability and scalability. After fine-tuning with UARPO, FinZero achieves an approximate 13.48\% improvement in prediction accuracy over GPT-4o in the high-confidence group, demonstrating the effectiveness of reinforcement learning fine-tuning in multimodal large model, including in financial time series forecasting tasks. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08742 |
| By: | Alain Guay; Dalibor Stevanovic |
| Abstract: | This paper introduces a tensor singular value decomposition (TSVD) approach for estimating non-Gaussian Structural Vector Autoregressive (SVAR) models. The proposed methodology applies to both complete and partial identification of structural shocks. The estimation procedure relies on third- and/or fourth-order cumulants. We establish the asymptotic distribution of the estimator and conduct a simulation study to evaluate its finite-sample performance. The results demonstrate that the estimator is highly competitive in small samples compared to alternative methods under complete identification. In cases of partial identification, the estimator also exhibits very good performance in small samples. To illustrate the practical relevance of the procedure under partial identification, two empirical applications are presented. Cet article introduit une approche de décomposition en valeurs singulières tensorielles (TSVD) pour l’estimation des modèles vectoriels autorégressifs structurels (SVAR) non gaussiens. La méthodologie proposée s’applique aussi bien à l’identification complète qu’à l’identification partielle des chocs structurels. La procédure d’estimation repose sur les cumulants d’ordre trois et/ou quatre. Nous établissons la distribution asymptotique de l’estimateur et menons une étude de simulation afin d’évaluer ses performances en petits échantillons. Les résultats démontrent que l’estimateur est particulièrement compétitif dans les petits échantillons par rapport aux méthodes alternatives en cas d’identification complète. Dans les situations d’identification partielle, l’estimateur présente également de très bonnes performances en petits échantillons. Afin d’illustrer la pertinence pratique de la procédure en contexte d’identification partielle, deux applications empiriques sont présentées. |
| Keywords: | Non-Gaussian SVAR, tensor decomposition, cumulants, SVAR non gaussien, décomposition tensorielle, cumulants |
| JEL: | C12 C32 C51 |
| Date: | 2025–09–02 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2025s-26 |
| By: | Ziyao Wang; Svetlozar T Rachev |
| Abstract: | Financial returns are known to exhibit heavy tails, volatility clustering and abrupt jumps that are poorly captured by classical diffusion models. Advances in machine learning have enabled highly flexible functional forms for conditional means and volatilities, yet few models deliver interpretable state--dependent tail risk, capture multiple forecast horizons and yield distributions amenable to backtesting and execution. This paper proposes a neural L\'evy jump--diffusion framework that jointly learns, as functions of observable state variables, the conditional drift, diffusion, jump intensity and jump size distribution. We show how a single shared encoder yields multiple forecasting heads corresponding to distinct horizons (daily, weekly, etc.), facilitating multi--horizon density forecasts and risk measures. The state vector includes conventional price and volume features as well as novel complexity measures such as permutation entropy and recurrence quantification analysis determinism, which quantify predictability in the underlying process. Estimation is based on a quasi--maximum likelihood approach that separates diffusion and jump contributions via bipower variation weights and incorporates monotonicity and smoothness regularisation to ensure identifiability. A cost--aware portfolio optimiser translates the model's conditional densities into implementable trading strategies under leverage, turnover and no--trade--band constraints. Extensive empirical analyses on cross--sectional equity data demonstrate improved calibration, sharper tail control and economically significant risk reduction relative to baseline diffusive and GARCH benchmarks. The proposed framework is therefore an interpretable, testable and practically deployable method for state--dependent risk and density forecasting. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01041 |
| By: | Yufei Sun (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | This thesis examines market-neutral, mean-reversion-based statistical arbitrage strategies in the Chinese equity market, using two factor decomposition methods: Principal Component Analysis (PCA) and sector-based Exchange-Traded Funds (ETFs). Residual returns are modeled as mean-reverting Ornstein–Uhlenbeck (OU) processes, generating contrarian signals. A 60-day rolling window ensures out-of-sample estimation. Realistic frictions are included via a 10-basis-point round-trip cost. Backtests from 2005 to 2024 compare four configurations: synthetic ETFs, fixed PCA, dynamic PCA, and trading-time volume adjustments. Both PCA- and ETF-based strategies deliver robust Sharpe ratios near 0.90–0.95. PCA portfolios perform better under high cross-sectional volatility, while ETF-based models remain stable during structural shifts. Incorporating trading volume enhances returns, especially for ETF models. Sensitivity analysis highlights the importance of threshold tuning and rolling-window lengths. These findings stress the critical role of factor construction and signal design in market-neutral strategies, suggesting further improvement via adaptive PCA and volume-weighted signals. |
| Keywords: | Quantitative Trading, Pair Trading, Principal Component Analysis, Chinese Stock Market |
| JEL: | C32 C58 G11 G12 G17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-21 |
| By: | Hugo Kruiniger |
| Abstract: | This paper proposes new inference methods for panel AR models with arbitrary initial conditions and heteroskedasticity and possibly additional regressors that are robust to the strength of identification. Specifically, we consider several Maximum Likelihood based methods of constructing tests and confidence sets (CSs) and show that (Quasi) LM tests and CSs that use the expected Hessian rather than the observed Hessian of the log-likelihood have correct asymptotic size (in a uniform sense). We derive the power envelope of a Fixed Effects version of such a LM test for hypotheses involving the autoregressive parameter when the average information matrix is estimated by a centered OPG estimator and the model is only second-order identified, and show that it coincides with the maximal attainable power curve in the worst case setting. We also study the empirical size and power properties of these (Quasi) LM tests and CSs. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20855 |
| By: | Sourish Das |
| Abstract: | This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-linear function, allowing tail risk to respond dynamically to market volatility. We examine four prior choices for Bayesian regularisation of regression coefficients: Cauchy, Lasso (Laplace), Ridge (Gaussian), and Zellner's g-prior. Simulation results suggest that the Cauchy prior delivers the best trade-off between predictive accuracy and model simplicity, achieving the lowest RMSE, AIC, and BIC values. Empirically, we apply the model to large negative returns (exceeding 5%) in the Nifty 50 index. Volatility measures from the Nifty 50, S&P 500, and gold are used as covariates to capture both domestic and global risk drivers. Our findings show that tail risk increases significantly with higher market volatility. In particular, both S&P 500 and gold volatilities contribute meaningfully to crash prediction, highlighting global spillover and flight-to-safety effects. The proposed GPR model offers a robust and interpretable approach for tail risk forecasting in emerging markets. It improves upon traditional EVT-based models by incorporating real-time financial indicators, making it useful for practitioners, policymakers, and financial regulators concerned with systemic risk and stress testing. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.17549 |
| By: | Marcelo C. Medeiros; Jeronymo M. Pinro |
| Abstract: | The forecasting combination puzzle is a well-known phenomenon in forecasting literature, stressing the challenge of outperforming the simple average when aggregating forecasts from diverse methods. This study proposes a Reinforcement Learning - based framework as a dynamic model selection approach to address this puzzle. Our framework is evaluated through extensive forecasting exercises using simulated and real data. Specifically, we analyze the M4 Competition dataset and the Survey of Professional Forecasters (SPF). This research introduces an adaptable methodology for selecting and combining forecasts under uncertainty, offering a promising advancement in resolving the forecasting combination puzzle. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20795 |
| By: | Peiyun Jiang; Yoshimasa Uematsu; Takashi Yamagata |
| Abstract: | In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{\alpha _{k}}$, $0 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.02066 |
| By: | Noah Shore |
| Abstract: | The Coherent Multiplex is formalized and validated as a scalable, real-time system for identifying, analyzing, and visualizing coherence among multiple time series. Its architecture comprises a fast spectral similarity layer based on cosine similarity metrics of Fourier-transformed signals, and a sparse time-frequency layer for wavelet coherence. The system constructs and evolves a multilayer graph representing inter-signal relationships, enabling low-latency inference and monitoring. A simulation prototype demonstrates functionality across 8 synthetic channels with a high similarity threshold for further computation, with additional opportunities for scaling the architecture up to support thousands of input signals with constrained hardware. Applications discussed include neuroscience, finance, and biomedical signal analysis. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.19994 |
| By: | Francis J. DiTraglia; Laura Liu |
| Abstract: | This paper proposes a simple, novel, and fully-Bayesian approach for causal inference in partially linear models with high-dimensional control variables. Off-the-shelf machine learning methods can introduce biases in the causal parameter known as regularization-induced confounding. To address this, we propose a Bayesian Double Machine Learning (BDML) method, which modifies a standard Bayesian multivariate regression model and recovers the causal effect of interest from the reduced-form covariance matrix. Our BDML is related to the burgeoning frequentist literature on DML while addressing its limitations in finite-sample inference. Moreover, the BDML is based on a fully generative probability model in the DML context, adhering to the likelihood principle. We show that in high dimensional setups the naive estimator implicitly assumes no selection on observables--unlike our BDML. The BDML exhibits lower asymptotic bias and achieves asymptotic normality and semiparametric efficiency as established by a Bernstein-von Mises theorem, thereby ensuring robustness to misspecification. In simulations, our BDML achieves lower RMSE, better frequentist coverage, and shorter confidence interval width than alternatives from the literature, both Bayesian and frequentist. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.12688 |