About

Seung Ahn's main research areas are panel data analysis, empirical asset pricing models, and factor models.

Research topics

• Computer Science
• Mathematics
• Econometrics
• Statistics
• Artificial Intelligence
• Applied mathematics

Selected publications

• Likelihood-based inference for dynamic panel data models

  Empirical Economics · 2023 · 11 citations

  1st authorCorresponding
  • Computer Science
  • Mathematics
  • Statistics
  PublisherDOI

• Estimation of Panel Data Models with Cross-Sectionally Heteroskedastic Data

  SSRN Electronic Journal · 2023-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Likelihood-based inference for dynamic panel data models

  Advanced studies in theoretical and applied econometrics · 2023-03-05

  book-chapter1st authorCorresponding
  PublisherDOI

• Model Selection for General Multi-Level Group Factor Models with Global, Regional and Local Factors

  SSRN Electronic Journal · 2023

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence
  PublisherDOI

• Forecasting with Partial Least Squares When a Large Number of Predictors Are Available

  SSRN Electronic Journal · 2022 · 10 citations

  1st authorCorresponding
  • Computer Science
  • Mathematics
  • Statistics
  PublisherDOI

• Beta Matrix and Common Factors in Stock Returns

  Journal of Financial and Quantitative Analysis · 2018-03-19 · 33 citations

  article1st authorCorresponding

  We consider the estimation methods for the rank of a beta matrix corresponding to a multifactor model and study which method would be appropriate for data with a large number of assets. Our simulation results indicate that a restricted version of Cragg and Donald’s (1997) Bayesian information criterion estimator is quite reliable for such data. We use this estimator to analyze some selected asset pricing models with U.S. stock returns. Our results indicate that the beta matrix from many models fails to have full column rank, suggesting that risk premiums in these models are underidentified.

  PublisherDOI

• Erratum to: Life-cycle consumption, precautionary saving, and risk sharing: an integrated analysis using household panel data

  The B E Journal of Macroeconomics · 2018-01-01

  erratum1st author
  PublisherDOI

• Asset Pricing and Excess Returns Over the Market Return

  SSRN Electronic Journal · 2017-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Is there a missing factor? A canonical correlation approach to factor models

  Review of Financial Economics · 2017-12-13 · 1 citations

  article1st author

  Abstract A common question in asset pricing research is if a finite set of observable variables can completely capture the systematic or common variations in a large number of response variables. This paper provides a new approach to answer this question. A novelty is that common factors are extracted using canonical relations between response variables and observable factors. We show how these factors in combination with tests for the number of factors can be used to evaluate if a given set of macroeconomic and financial variables is sufficient to capture all the systematic variation in the response variables. We illustrate the usefulness of our methods by analyzing the systematic determinants of credit spreads of U.S. corporate bonds.

  PublisherDOI

• Asset Pricing and Excess Returns over the Market Return

  RePEc: Research Papers in Economics · 2017-09-20

  preprint1st authorCorresponding

  Some studies have found that the estimated market betas from multi-factor models have much smaller cross-sectional variations than those from the Capital Asset Pricing Model. This paper provides a theoretical explanation for this empirical finding. For the cases in which the market portfolio (of stocks) is a well-diversified but mean-variance inefficient one, we show that the market betas become unitary when the Capital Asset Pricing Model is augmented with the common factors in the space of excess returns. Consequently, the market betas have no power to explain the cross-sectional variation of expected stock returns. Based on this finding, we propose an alternative method that can identify the relevant factors for asset pricing. Specifically, we show that the relevant factors can be extracted by the principal components from a large set of excess stock returns over the market return. Analyzing US data on individual and portfolio stock returns, we develop a benchmark model with five principal component factors. We use the model to study if the five-factor model of Fama and French (2015) captures all the relevant information to span the space of excess returns. We find that the Fama-French model contains a large fraction of the relevant information, but there is still some room for improvement.

  Publisher

Frequent coauthors

• M. Fabricio Perez

  Wilfrid Laurier University

  13 shared

• Young Hoon Lee

  13 shared

• Peter Schmidt

  Michigan State University

  12 shared

• Christopher Gadarowski

  12 shared

• Alex R. Horenstein

  John von Neumann University

  8 shared

• Hyungsik Roger Moon

  Yonsei University

  8 shared

• Josef C. Brada

  4 shared

• Stephan Dieckmann

  4 shared

Labs

• Econometric ModelingPI

Education

• Ph.D.

  Michigan State University

  1990