Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency

By Ilze Kalniņa, D. Xiu. 2017

55 citations recorded by the source corpus.

ABSTRACT We consider two new approaches to nonparametric estimation of the leverage effect. The first approach uses stock prices alone. The second approach uses the data on stock prices as well as a certain volatility instrument, such as the Chicago Board Options Exchange (CBOE) volatility index (VIX) or the Black–Scholes implied volatility. The theoretical justification for the instrument-based estimator relies on a certain invariance property, which can be exploited when high-frequency data are available. The price-only estimator is more robust since it is valid under weaker assumptions. However, in the presence of a valid volatility instrument, the price-only estimator is inefficient as the instrument-based estimator has a faster rate of convergence.We consider an empirical application, in which we study the relationship between the leverage effect and the debt-to-equity ratio, credit risk, and illiquidity. Supplementary materials for this article are available online.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/0a37810ed6b20689967994980d8284ba03dcc5cb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. D. Xiu
2. V. Todorov
3. Yacine Aït-Sahalia
4. George Tauchen
5. Jia Li
6. Ilze Kalniņa
7. Markus Bibinger
8. Tim Bollerslev

In Finance

1. V. Todorov
2. D. Xiu
3. Yacine Aït-Sahalia
4. George Tauchen
5. Jia Li
6. Ilze Kalniņa
7. Tim Bollerslev
8. Jean Jacod

Citations in the corpus

• Principal Component Analysis of High-Frequency Data (2016, background): “…there is a second-order asymptotic bias associated with the estimator (6), a complication that is also encountered in other situations such as the quarticity estimator in Jacod and Rosenbaum (2013), the GMM estimator in Li and Xiu (2016), or the leverage effect estimator by Kalnina and Xiu (2017).”
• High-frequency factor models and regressions (2020, background): “Jacod and Rosenbaum (2013) develop a general inference framework that is useful to study the continuous components of more general regressions with time-varying betas, see also Li et al. (2016) and Kalnina and Xiu (2017).”
• Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence (2014, unknown): “Spot volatility estimation is relevant also for multiple-step approaches to perform inference on functionals that hinge on the volatility, see, for example, Kalnina and Xiu (2017) and Li and Xiu (2016).”
• Adaptive estimation of continuous-time regression models using high-frequency data (2017, methodology): “, 2015) estimation of integrated nonlinear transforms of volatility for univariate transforms such as log(·), √ · , exp(·) and multivariate transforms such as the correlation coefficient (Kalnina and Xiu, 2017), the idiosyncratic variance (Li et al.”