|
on Econometric Time Series |
| By: | Matteo Barigozzi; Luca Trapin |
| Abstract: | This paper considers an approximate dynamic matrix factor model that accounts for the time series nature of the data by explicitly modelling the time evolution of the factors. We study Quasi Maximum Likelihood estimation of the model parameters based on the Expectation Maximization (EM) algorithm, implemented jointly with the Kalman smoother which gives estimates of the factors. This approach allows to easily handle arbitrary patterns of missing data. We establish the consistency of the estimated loadings and factor matrices as the sample size $T$ and the matrix dimensions $p_1$ and $p_2$ diverge to infinity. The finite sample properties of the estimators are assessed through a large simulation study and an application to a financial dataset of volatility proxies. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.04112 |
| By: | Yaming Chang |
| Abstract: | This paper applies the realized exponential generalized autoregressive conditional heteroskedasticity (REGARCH) model to analyze the Nikkei 225 index from 2010 to 2017, utilizing realized variance (RV) and realized range-based volatility (RRV) as high-frequency measures of volatility. The findings show that REGARCH models outperform standard GARCH family models in both in-sample fitting and out-of-sample forecasting, driven by the dynamic information embedded in high-frequency realized measures. Incorporating multiple realized measures within a joint REGARCH framework further enhances model performance. Notably, RRV demonstrates superior predictive power compared to RV, as evidenced by improvements in forecast accuracy metrics. Moreover, the forecasting results remain robust under both rolling-window and recursive evaluation schemes. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02695 |
| By: | Blazsek, Szabolcs; Ayala, Astrid |
| Abstract: | Score-driven filters are updated by the scaled gradient of the log-likelihood (LL). The gradient is with respect to a dynamic parameter and the scaling parameter is 1, or the information quantity or its square root in the literature. The information quantity is minus the expected value of the Hessian of the LL with respect to a dynamic parameter, i.e. the Hessianis smoothed using a probability-weighted average for each period. We suggest an alternative approach and scale the gradients using novel Hessian-driven filters, i.e. Hessian smoothing is performed over time. The method can be used for score-driven models in general. We illustrate it for Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity). Weuse Standard & Poor's 500 (S&P 500) data. We show empirical results for in-sample statistical performance from 2015 to 2025, and out-of-sample forecasting performance from 2021 to 2025. We find for the S&P 500 that the Hessian-driven scaling is superior to the existing scaling methods for Beta-t-EGARCH. We find similar results for a Monte Carlo simulation experimentwhere misspecified Beta-t-EGARCH models with constant and Hessian-driven gradient scaling are estimated for returns generated by a Markov-switching (MS) Beta-t-EGARCH. Hessianbased gradient scaling captures regime-switching dynamics better than constant gradient scaling. |
| Keywords: | Dynamic conditional score (DCS); Generalized autoregressive score (GAS); Dynamic gradient scaling parameters in score driven filters; Gradient descent; Newton's method |
| JEL: | C22 C32 |
| Date: | 2025–02–17 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:45978 |
| By: | Jean-Yves Pitarakis |
| Abstract: | This paper introduces a new method for testing the statistical significance of estimated parameters in predictive regressions. The approach features a new family of test statistics that are robust to the degree of persistence of the predictors. Importantly, the method accounts for serial correlation and conditional heteroskedasticity without requiring any corrections or adjustments. This is achieved through a mechanism embedded within the test statistics that effectively decouples serial dependence present in the data. The limiting null distributions of these test statistics are shown to follow a chi-square distribution, and their asymptotic power under local alternatives is derived. A comprehensive set of simulation experiments illustrates their finite sample size and power properties. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00475 |