Macro Factors in Bond Risk Premia

By Sydney C. Ludvigson, Serena Ng. 2005

1,045 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, methodology): “Similar datasets have been extensively studied in the forecasting literature (Bai and Ng, 2008; Ludvigson and Ng, 2009).”
Expected Returns in Treasury Bonds (2015, neutral): “Subsequently, a large literature studies the link between term premia, and macro-finance variables (Cooper and Priestley, 2009; Huang and Shi, 2010; Fontaine and Garcia, 2011; Joslin, Priebsch, and Singleton, 2012; Ludvigson and Ng, 2009).”
Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012, methodology): “Detailed description of the series as well as the transformations required to make the series I(0) can be found in appendix A of Ludvigson and Ng (2009).”
Bond Variance Risk Premia (2012, positive): “As in Ludvigson and Ng (2009), it is reasonable to interpret the first factor as a real factor.”
Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry (2015, neutral): “Finally, we consider macroeconomic factors that have previously been shown to affect stock returns and bond risk premia [8, 32, 96].”
Downside Variance Risk Premium (2015, positive): “Following Ludvigson and Ng (2009), we survey the correlations of variance, upside variance, downside variance, and skewness risk premia with 124 financial and economic indicators.”
A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, neutral): “The monthly predictor variables are an updated version of the 132 macroeconomic series used in Ludvigson and Ng (2009) and extended by Jurado et al. (2014) to December 2011.”
Large Dimensional Factor Analysis (2008, background): “(2005), Marcellino et al. (2003), Schumacher (2005), Forni et al.”
Forecasting US Recessions: The Role of Sentiment (2014, positive): “Further, as pointed out by Ludvigson and Ng (2009), any labeling of the factors will be imperfect as each factor is to some degree influenced by all panel variables and the orthogonalization implies that none of the factors will correspond exactly to a precise economic concept.”
The Three-Pass Regression Filter: A New Approach to Forecasting Using Many Predictors (2015, neutral): “Ng (2008, 2009) and Ludvigson and Ng (2009). Any variable that we eventually target is removed from the set of predictors.”
Bond Market Exposures to Macroeconomic and Monetary Policy Risks (2017, neutral): “The findings on the left (the top and bottom left plots) are consistent with what Piazzesi and Schneider (2006) report.”
Deep Learning in Asset Pricing (2019, methodology): “Messmer (2017) and Feng, He, and Polson (2018) follow a similar approach as Gu, Kelly, and Xiu (2020) to predict stock returns with neural networks. Bianchi, Büchner, and Tamoni (2019) provide a comparison of machine learning method for predicting bond returns in the spirit of Gu, Kelly, and Xiu (20”