The empirical risk–return relation: A factor analysis approach ☆

By Sydney C. Ludvigson, Serena Ng. 2006

658 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Sufficient Forecasting Using Factor Models (2015, background): “y a few papers in forecasting a nonlinear time series using factor models. Bai and Ng (2008) discussed the use of squared factors (i.e., volatility of the factors) in augmenting forecasting equation. Ludvigson and Ng (2007) found that the square of the rst factor estimated from a set of nancial fact”
Transition Modeling and Econometric Convergence Tests (2007, neutral): “[31] Ludvigson, S.”
Time-Varying Risk Premium in Large Cross-Sectional Equity Data Sets (2016, background): “Our approach is further motivated by the potential loss of information and bias induced by grouping stocks to build portfolios in asset pricing tests (Litzenberger and Ramaswamy (1979), Lo and MacKinlay (1990), Berk (2000), Conrad, Cooper and Kaul (2003), Phalippou (2007)). Avramov and Chordia (2006”
Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014, neutral): “…RVOL and DFY load most heavily on F̂ ECON21 t ; accordingly, F̂ ECON 21 t spikes during the global financial crisis in Figure 1, panel B, when stock 5 Ludvigson and Ng (2007, 2009) estimate predictive regressions for excess stock and bond returns, respectively, based on principal components…”
Deep Learning in Asset Pricing (2019, methodology): “A simple example would be the factor model in Ludvigson and Ng (2009) where the information in a macroeconomic data set very similar to ours is summarized by a small number of PCA factors.”
Uncertainty Shocks as Second-Moment News Shocks (2017, methodology): “In the fifth column, we also try adding principal components from the large set of financial and macroeconomic time series collected by Ludvigson and Ng (2007) (which would represent using a FAVAR rather than a pure VAR).”
EFFICIENT ESTIMATION OF FACTOR MODELS (2011, neutral): “…2005; Forni, Hallin, Lippi, and Reichlin, 2003; Huang, Lee, and Li, 2006), policy analysis (e.g., Favero, Marcellino, and Neglia, 2005; Bernanke, Boivin, and Eliasz, 2005), predictive regressions (e.g., Ludvigson and Ng, 2007), and instrumental variables estimation (cf. Bai and Ng, 2010).”
Forecasting economic time series using targeted predictors (2008, background): “Ludvigson and Ng (2005) found that the square of the first factor estimated from a set of financial factors (ie.”
On factor models with random missing: EM estimation, inference, and cross validation (2020, methodology): “One simple way to handle missing data is to omit the cross-sectional units with missing values; see, e.g., Ludvigson and Ng (2007).”
Investor Attention and Stock Returns (2020, neutral): “This approach has been widely used in the literature on stock return predictability, such as in articles by Baker and Wurgler (2006), Ludvigson and Ng (2007), and Neely, Rapach, Tu, and Zhou (2014), among many others.”
Selecting Instrumental Variables in a Data Rich Environment (2009, methodology): “in Ludvigson and Ng (2007), which consist of quarterly observations on a panel of 209 macroeconomic series for the sample 1960:1-2002:4, where some are monthly series aggregated to quarterly levels.”
Application of data mining techniques in stock markets: A survey (2010, methodology): “In doing so, their study contributes to the empirical literature by evaluating both the potential role of omitted information in the estimated risk–return relation as well as the robustness of previous results to conditioning on richer information sets (Sydney and Serena, 2007).”