|
on Econometric Time Series |
|
Issue of 2025–12–01
fifteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Amaze Lusompa |
| Abstract: | It is well known that model selection via cross validation can be biased for time series models. However, many researchers have argued that this bias does not apply when using cross-validation with vector autoregressions (VAR) or with time series models whose errors follow a martingale-like structure. I show that even under these circumstances, performing cross-validation on time series data will still generate bias in general. |
| Keywords: | time series; model selection; model validation; martingale |
| JEL: | C52 C50 C10 |
| Date: | 2025–11–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:102151 |
| By: | Mountford, Andrew |
| Abstract: | Restrictions on the contemporaneous effects matrix used to identify fundamental shocks in a structural VAR, also determine the mapping from the structural constant terms to the reduced form constant terms. In some models one will have priors about these structural constant terms and these should therefore be included in a Bayesian estimation procedure. We illustrate the significance of this using a standard 3 variable VAR estimated in Baumeister and Hamilton (2018). We show that imposing priors over the structural constant terms can lead to a more intuitive estimated monetary policy rule and a larger role for monetary policy in describing the evolution of the data, particularly for inflation. |
| Keywords: | Vector Autoregressions, Historical Decompositions, Monetary Policy |
| JEL: | C32 E00 E50 |
| Date: | 2025–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126806 |
| By: | Ferreira Batista Martins, Igor (Örebro University School of Business); Virbickaitè, Audronè (CUNEF University, Madrid, Spain); Nguyen, Hoang (Linköping University); Freitas Lopes, Hedibert (nsper Institute of Education and Research, Sao Paulo, Brazil) |
| Abstract: | We propose a high-frequency stochastic volatility model that integrates persistent component, intraday periodicity, and volume-driven time-of-day effects. By allowing intraday volatility patterns to respond to lagged trading activity, the model captures economically and statistically relevant departures from traditional intraday seasonality effects. We find that the volumedriven component accounts for a substantial share of intraday volatility for futures data across equity indexes, currencies, and commodities. Out-of-sample, our forecasts achieve near-zero intercepts, unit slopes, and the highest R2 values in Mincer-Zarnowitz regressions, while horserace regressions indicate that competing forecasts add little information once our predictions are included. These statistical improvements translate into economically meaningful gains, as volatility-managed portfolio strategies based on our model consistently improve Sharpe ratios. Our results highlight the value of incorporating lagged trading activity into high-frequency volatility models. |
| Keywords: | Intraday volatility; high-frequency; volume; periodicity. |
| JEL: | C11 C22 C53 C58 |
| Date: | 2025–11–21 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_014 |
| By: | Toru Kitagawa; Yizhou Kuang |
| Abstract: | Leaving posterior sensitivity concerns aside, non-identifiability of the parameters does not raise a difficulty for Bayesian inference as far as the posterior is proper, but multi-modality or flat regions of the posterior induced by the lack of identification leaves a challenge for modern Bayesian computation. Sampling methods often struggle with slow or non-convergence when dealing with multiple modes or flat regions of the target distributions. This paper develops a novel Markov chain Monte Carlo (MCMC) approach for non-identified models, leveraging the knowledge of observationally equivalent sets of parameters, and highlights an important role that identification plays in modern Bayesian analysis. We show that our proposal overcomes the issues of being trapped in a local mode and achieves a faster rate of convergence than the existing MCMC techniques including random walk Metropolis-Hastings and Hamiltonian Monte Carlo. The gain in the speed of convergence is more significant as the dimension or cardinality of the identified sets increases. Simulation studies show its superior performance compared to other popular computational methods including Hamiltonian Monte Carlo and sequential Monte Carlo. We also demonstrate that our method uncovers non-trivial modes in the target distribution in a structural vector moving-average (SVMA) application. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12847 |
| By: | Xuzhu ZHENG (Graduate School of Economics, The University of Osaka); Masato UBUKATA (The Faculty of Economics, Meiji Gakuin University); Kosuke OYA (Graduate School of Economics, The University of Osaka, Center for Mathematical Modeling and Data Science, The University of Osaka) |
| Abstract: | This study examines the existence of rough volatility, which has recently attracted considerable attention and is characterized by volatility dynamics that cannot be fully captured by conventional volatility models. Specifically, we investigate whether the observed roughness in volatility is merely an artifact induced by microstructure noise inherent in high-frequency price data, or whether such rough behavior persists even after accounting for the effects of noise. The empirical analysis utilizes high-frequency data from the Nikkei 225 index as well as two representatives, actively traded individual stocks. Applying several representative volatility estimation methods, we first construct volatility series and then estimate their Hurst exponents using a nonparametric estimation procedure proposed in the literature. Our results show that, regardless of the presence of microstructure noise, the estimated Hurst exponents consistently take low values, suggesting that the volatility processes under study exhibit rough behavior. These findings provide supporting evidence for the necessity of incorporating roughness into volatility modeling to achieve a more refined understanding of volatility dynamics in financial markets. |
| Keywords: | High-frequency data, Volatility, Roughness |
| JEL: | C14 C55 C58 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2517 |
| By: | Masahiro Tanaka |
| Abstract: | We develop a computationally efficient framework for quasi-Bayesian inference based on linear moment conditions. The approach employs a delayed acceptance Markov chain Monte Carlo (DA-MCMC) algorithm that uses a surrogate target kernel and a proposal distribution derived from an approximate conditional posterior, thereby exploiting the structure of the quasi-likelihood. Two implementations are introduced. DA-MCMC-Exact fully incorporates prior information into the proposal distribution and maximizes per-iteration efficiency, whereas DA-MCMC-Approx omits the prior in the proposal to reduce matrix inversions, improving numerical stability and computational speed in higher dimensions. Simulation studies on heteroskedastic linear regressions show substantial gains over standard MCMC and conventional DA-MCMC baselines, measured by multivariate effective sample size per iteration and per second. The Approx variant yields the best overall throughput, while the Exact variant attains the highest per-iteration efficiency. Applications to two empirical instrumental variable regressions corroborate these findings: the Approx implementation scales to larger designs where other methods become impractical, while still delivering precise inference. Although developed for moment-based quasi-posteriors, the proposed approach also extends to risk-based quasi-Bayesian formulations when first-order conditions are linear and can be transformed analogously. Overall, the proposed algorithms provide a practical and robust tool for quasi-Bayesian analysis in statistical applications. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.17117 |
| By: | Jutta G. Kurth; Jean-Philippe Bouchaud |
| Abstract: | We derive the stationary distribution in various regimes of the extended Chiarella model of financial markets. This model is a stochastic nonlinear dynamical system that encompasses dynamical competition between a (saturating) trending and a mean-reverting component. We find the so-called mispricing distribution and the trend distribution to be unimodal Gaussians in the small noise, small feedback limit. Slow trends yield Gaussian-cosh mispricing distributions that allow for a P-bifurcation: unimodality occurs when mean-reversion is fast, bimodality when it is slow. The critical point of this bifurcation is established and refutes previous ad-hoc reports and differs from the bifurcation condition of the dynamical system itself. For fast, weakly coupled trends, deploying the Furutsu-Novikov theorem reveals that the result is again unimodal Gaussian. For the same case with higher coupling we disprove another claim from the literature: bimodal trend distributions do not generally imply bimodal mispricing distributions. The latter becomes bimodal only for stronger trend feedback. The exact solution in this last regime remains unfortunately beyond our proficiency. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.13277 |
| By: | M. Stocker; W. Ma{\l}gorzewicz; M. Fontana; S. Ben Taieb |
| Abstract: | Conformal prediction is a powerful post-hoc framework for uncertainty quantification that provides distribution-free coverage guarantees. However, these guarantees crucially rely on the assumption of exchangeability. This assumption is fundamentally violated in time series data, where temporal dependence and distributional shifts are pervasive. As a result, classical split-conformal methods may yield prediction intervals that fail to maintain nominal validity. This review unifies recent advances in conformal forecasting methods specifically designed to address nonexchangeable data. We first present a theoretical foundation, deriving finite-sample guarantees for split-conformal prediction under mild weak-dependence conditions. We then survey and classify state-of-the-art approaches that mitigate serial dependence by reweighting calibration data, dynamically updating residual distributions, or adaptively tuning target coverage levels in real time. Finally, we present a comprehensive simulation study that compares these techniques in terms of empirical coverage, interval width, and computational cost, highlighting practical trade-offs and open research directions. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.13608 |
| By: | Silvia Onofri; Andrey Shternshis; Stefano Marmi |
| Abstract: | Markets efficiency implies that the stock returns are intrinsically unpredictable, a property that makes markets comparable to random number generators. We present a novel methodology to investigate ultra-high frequency financial data and to evaluate the extent to which tick by tick returns resemble random sequences. We extend the analysis of ultra high-frequency stock market data by applying comprehensive sets of randomness tests, beyond the usual reliance on serial correlation or entropy measures. Our purpose is to extensively analyze the randomness of these data using statistical tests from standard batteries that evaluate different aspects of randomness. We illustrate the effect of time aggregation in transforming highly correlated high-frequency trade data to random streams. More specifically, we use many of the tests in the NIST Statistical Test Suite and in the TestU01 battery (in particular the Rabbit and Alphabit sub-batteries), to prove that the degree of randomness of financial tick data increases together with the increase of the aggregation level in transaction time. Additionally, the comprehensive nature of our tests also uncovers novel patterns, such as non-monotonic behaviors in predictability for certain assets. This study demonstrates a model-free approach for both assessing randomness in financial time series and generating pseudo-random sequences from them, with potential relevance in several applications. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.17479 |
| By: | Yasuyuki Matsumura |
| Abstract: | Principal component analysis (PCA) is frequently employed as a dimension reduction tool when the number of covariates is large. However, the number of principal components to be retained in PCA is typically determined in a researcher-dependent manner. To mitigate the subjectivity in PCA, this paper proposes a data-driven testing procedure to estimate the number of underlying principal components. While existing work such as G'Sell et al. (2016), Taylor et al. (2016) and Choi et al. (2017) discuss similar tests under fixed design, this paper investigates an extension of their framework to a more general econometric setup with the random design. The proposed test is proved to achieve asymptotically exact type 1 error controls under a locally defined null hypothesis, with simulation examples indicating an asymptotic validity of our test. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.10419 |
| By: | Ping Wu; Dan Zhu |
| Abstract: | Financial markets are interconnected, with micro-currents propagating across global markets and shaping economic trends. This paper moves beyond traditional stock market indices to examine cross-sectional return distributions-15 in our empirical application, each representing a distinct global market. To facilitate this analysis, we develop a matrix functional VAR method with interpretable factors extracted from cross-sectional return distributions. Our approach extends the existing framework from modeling a single function to multiple functions, allowing for a richer representation of cross-sectional dependencies. By jointly modeling these distributions with U.S. macroeconomic indicators, we uncover the predictive power of financial market in forecasting macro-economic dynamics. Our findings reveal that U.S. contractionary monetary policy not only lowers global stock returns, as traditionally understood, but also dampens cross-sectional return kurtosis, highlighting an overlooked policy transmission. This framework enables conditional forecasting, equipping policymakers with a flexible tool to assess macro-financial linkages under different economic scenarios. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.17140 |
| By: | boughabi, houssam |
| Abstract: | The paper explains how inflation, monetary policy, and fiscal interventions interacted in Ghana from 2005 to 2014. A discrete-time macroeconomic model with money supply, taxation, household consumption, GDP per capita, and price adjustments as variables has been developed. The paper uses FIGARCH and GARCH models to investigate the volatility of inflation to decide if it has long-memory properties. The empirical findings show that the fractional differencing parameter $d = 0$ (Hurst exponent $H = 0.5$), which means that there is no persistent long-range dependence in inflation volatility. Hence, a standard GARCH(1, 1) model is sufficient to describe short-term volatility dynamics, and shocks to conditional variance occur immediately but subside rapidly. Besides that, the research determines a monetary-fiscal neutrality threshold, which highlights the equilibrium where income growth balances the inflationary pressures; this threshold is assessed macroeconomically into general prices and compared to actual general prices to evaluate its validity. The results indicate that inflation in Ghana during this period is mainly of short-memory nature, thus reaffirming the role of short-term monetary and fiscal operations in price stabilization, and confirming the successful validation of the macroeconomic neutrality threshold linking income growth and price stability. |
| Keywords: | Inflation volatility, statistical modelling, monetary transmission, threshold modeling, Ghanaian economy |
| JEL: | C32 E31 E37 E44 O55 |
| Date: | 2025–10–29 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126757 |
| By: | Kéa Baret; Frédérique Bec; Marion Cochard |
| Abstract: | We propose a simple, simulation based framework for stochastic debt sustainability analysis. Estimating a parsimonious vector autoregression (frequentist and Bayesian) on quarterly French data (1990:Q1–2023:Q4) for the debt's key drivers, we generate predictive fan charts and probability statements for debt to GDP outcomes. Median VAR projections are close to a hypothetical deterministic baseline derived from the deterministic debt sustainability analysis framework. Assuming this illustrative central scenario, historical relationships estimated by our VAR models imply a corresponding confidence band around the debt trajectory. The BVAR yields slightly wider cones and lower tail probabilities than the frequentist VAR, with cone widths between those reported by the European Commission and the ECB. Our analysis, which does not reflect the most recent developments in public finance, suggests that an ambitious fiscal consolidation effort would be required to materially enhance the prospects of stabilizing the debt-to-GDP ratio over the medium term. |
| Keywords: | Debt Sustainability, Stochastic Analysis, VAR Model, Bayesian Forecasting, Density Forecasts |
| JEL: | C3 E6 H6 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:1019 |
| By: | Antonio Galvao (Michigan State University) |
| Abstract: | This paper studies identification, estimation, and inference of general unconditional quantile partial effects (UQPE) under endogeneity. When a valid instrument is available, we show that using a control-function approach, the UQPE can be identified through the conditional average of the conditional quantile partial effects, given the unconditional quantile of the dependent variable of interest. Based on this identification result, we propose a semiparametric two-step estimator. The first step is based on a control-function quantile regression method, and the second step uses a nonparametric estimator to compute the conditional average. This general formulation includes nonparametric regressions and sieve estimators. The asymptotic properties of the estimator are derived, namely, consistency and asymptotic normality. We also develop practical statistical inference procedures and establish the validity of a bootstrap approach. Monte Carlo simulations show that the proposed methods have good finite-sample properties. Finally, we apply the proposed methods to estimate unconditional quantile effects of class size on educational performance. |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:boc:econ25:02 |
| By: | Marina Khismatullina; Bernhard van der Sluis |
| Abstract: | This paper proposes a novel framework to test for slope heterogeneity between time-varying coefficients in panel data models. Our test not only allows us to detect whether the coefficient functions are the same across all units or not, but also determines which of them are different and where these differences are located. We establish the asymptotic validity of our multiscale test. As an extension of the proposed procedure, we show how to use the results to uncover latent group structures in the model. We apply our methods to test for heterogeneity in the effect of U.S. monetary shocks on 49 foreign economies and itself. We find evidence that such heterogeneity indeed exists and we discuss the clustering results for two groups. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12600 |