Understanding Systematic Risk: A High‐Frequency Approach
By Markus Pelger. Journal of Finance, 2020
50 citations recorded by the source corpus.
Under a large dimensional approximate factor model for asset returns, I use high-frequency data for the S&P 500 firms to estimate the latent continuous and jump factors. I estimate four very persistent continuous systematic factors for 2007 to 2012 and three from 2003 to 2006. These four continuous factors can be approximated very well by a market, an oil, a finance and an electricity portfolio. The value, size and momentum factors play no significant role in explaining these factors. For the time period 2003 to 2006 the finance factor seems to disappear. There exists only one persistent jump factor, namely a market jump factor. Using implied volatilities from option price data, I analyze the systematic factor structure of the volatilities. There is only one persistent market volatility factor, while during the financial crisis an additional temporary banking volatility factor appears. Based on the estimated factors, I can decompose the leverage effect, i.e. the correlation of the asset return with its volatility, into a systematic and an idiosyncratic component. The negative leverage effect is mainly driven by the systematic component, while it can be non-existent for idiosyncratic risk.
Subfields: Accounting, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3c2aa3a2faf99963d6ffc8ef2a4541b4bc67819f
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Citations in the corpus
- Deep Learning in Asset Pricing (2019, methodology): “ results suggest that the long and short leg have dierent weights in the SDF and this additional exibility improves the performance of the linear model. These ndings are also in line with Lettau and Pelger (2020) who extract linear factors from the extreme deciles of single sorted portfolios and sho”
- State-Varying Factor Models of Large Dimensions (2018, methodology): “We provide an answer to the relevant economic question for which specific times and states loadings are different.14 Pelger (2020) illustrated in an empirical study that it is important to identify when and how time-varying loadings change as this can provide valuable economic insights.”