Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis

Forni, Mario; Hallin, Marc; Lippi, Marco; Zaffaroni, Paolo

doi:10.1016/j.jeconom.2017.04.002

Cited by 88 publications

(185 citation statements)

References 26 publications

(26 reference statements)

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“…The results of Forni et al (2015a) establish the consistency, as n, T → ∞, of all those estimators. Note that the cross-sectional ordering of the panel has an impact on the selection of the 2-dimensional blocks in step (iv).…”

Section: Step 1: Estimating the Level-common And Level-idiosyncratic supporting

confidence: 52%

“…is the kth lag estimated autocovariance of returns and K(·) a suitable (see Forni et al (2015a) for details) kernel function with bandwidth…”

Section: Step 1: Estimating the Level-common And Level-idiosyncratic mentioning

confidence: 99%

“…Although considering all n! permutations is clearly unfeasible, in practice, as stressed by Forni et al (2015a), a few of them are enough to deliver stable averages (which therefore are matching the infeasible average over all n! permutations).…”

Section: Step 1: Estimating the Level-common And Level-idiosyncratic mentioning

confidence: 99%

“…As already mentioned, the consistency, as n and T tend to infinity, of all estimators derived in this section is carefully established in Forni et al (2015a), where they are computed from observed data. Here, those estimators are based on the estimated log-volatility proxies s T n and w T n obtained in Section 2.2.1.…”

Section: Step 2: Recovering the Market Volatility Shocksmentioning

confidence: 99%

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Generalized dynamic factor models and volatilities: estimation and forecasting

Barigozzi¹,

Hallin

2017

Journal of Econometrics

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In large panels of financial time series with dynamic factor structure on the levels or returns, the volatilities of the common and idiosyncratic components often exhibit strong correlations, indicating that both are exposed to the same market volatility shocks. This suggests, alongside the dynamic factor decomposition of returns, a dynamic factor decomposition of volatilities or volatility proxies. Based on this observation, Barigozzi and Hallin (2016) proposed an entirely non-parametric and model-free two-step general dynamic factor approach which accounts for a joint factor structure of returns and volatilities, and allows for extracting the market volatility shocks. Here, we go one step further, and show how the same two-step approach naturally produces volatility forecasts for the various stocks under study. In an applied exercise, we consider the panel of asset returns of the constituents of the S&P100 index over the period [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009]. Numerical results show that the predictors based on our two-step method outperform existing univariate and multivariate GARCH methods, as well as static factor GARCH models, in the prediction of daily high-low range-while avoiding the usual problems associated with the curse of dimensionality.JEL Classification: C32, C38, C58.

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Section: Step 1: Estimating the Level-common And Level-idiosyncratic supporting

confidence: 52%

“…is the kth lag estimated autocovariance of returns and K(·) a suitable (see Forni et al (2015a) for details) kernel function with bandwidth…”

Section: Step 1: Estimating the Level-common And Level-idiosyncratic mentioning

confidence: 99%

Section: Step 1: Estimating the Level-common And Level-idiosyncratic mentioning

confidence: 99%

Section: Step 2: Recovering the Market Volatility Shocksmentioning

confidence: 99%

See 2 more Smart Citations

Generalized dynamic factor models and volatilities: estimation and forecasting

Barigozzi¹,

Hallin

2017

Journal of Econometrics

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“…This model was proposed by Forni et al in [5], [6]. Differently from the previous models, here the assumption that the common component spans a finite-dimensional space is relaxed.…”

Section: The Fhlz Modelmentioning

confidence: 99%

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Marra

2017

Communications in Applied and Industrial Mathematics

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We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Reichlin (2000, 2005). The last model has been recently proposed by Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a insample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.

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Uncertainty and the predictability of stock returns

Cai

Pan

Wang

2021

Journal of Forecasting

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While several theoretical models imply that uncertainty has predictive ability for stock returns, few studies investigate this issue using empirical data. We fill this gap by comparing the predictive ability of uncertainty variables with the predictive ability of well‐known economic level variables. We find the in‐sample and out‐of‐sample return predictability using the combining uncertainty information. The predictability is significant from both economic and statistical perspectives. Further analysis shows that macroeconomic uncertainty and level information provide complementary predictive ability over the business cycle. We obtain stronger and more robust return predictability using both types of information together than using either source of information alone.

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Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis

Abstract: Key words and phrases: High -dimensional time series. Generalized dynamic factor models.Vector processes with singular spectral density. One-sided representations of dynamic factor models. Consistency and rates.

Cited by 88 publications

References 26 publications

Generalized dynamic factor models and volatilities: estimation and forecasting

Generalized dynamic factor models and volatilities: estimation and forecasting

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Uncertainty and the predictability of stock returns

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