6 papers
The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing
Kim Christensen, Martin Thyrsgaard, Bezirgen Veliyev
We propose a nonparametric estimator of the empirical distribution function (EDF) of the latent spot variance of the log-price of a financial asset. We show that over a fixed time…
Inference from high-frequency data: A subsampling approach
Kim Christensen, Mark Podolskij, Nopporn Thamrongrat +1
In this paper, we show how to estimate the asymptotic (conditional) covariance matrix, which appears in central limit theorems in high-frequency estimation of asset return volatili…
A machine learning approach to volatility forecasting
Kim Christensen, Mathias Siggaard, Bezirgen Veliyev
We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, includin…
Warp speed price moves: Jumps after earnings announcements
Kim Christensen, Allan Timmermann, Bezirgen Veliyev
Corporate earnings announcements unpack large bundles of public information that should, in efficient markets, trigger jumps in stock prices. Testing this implication is difficult…
A GMM approach to estimate the roughness of stochastic volatility
Anine E. Bolko, Kim Christensen, Mikko S. Pakkanen +1
We develop a GMM approach for estimation of log-normal stochastic volatility models driven by a fractional Brownian motion with unrestricted Hurst exponent. We show that a paramete…
Treatment Evaluation at the Intensive and Extensive Margins
Phillip Heiler, Asbjørn Kaufmann, Bezirgen Veliyev
This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bound…