Francesco Audrino
Title
Prof. Ph.D.
Last Name
Audrino
First name
Francesco
Email
francesco.audrino@unisg.ch
ORCID
Phone
+41 71 224 2431
SSRN
https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=333418
3 results
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Item type:Publication, Volatility Forecasting: Downside Risk, Jumps and Leverage EffectWe provide empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and propose a methodology to estimate the size of jumps in the quadratic variation. The leverage effect is separated into continuous and discontinuous effects and past volatility is separated into ``good" and ``bad" as well as into continuous and discontinuous risks. Using a long history of the S\&P500 price index, we find that the continuous leverage effect lasts about one week while the discontinuous leverage effect disappears after one day. ``Good" and ``bad" continuous risks both characterize the volatility persistence while ``bad" jump risk is much more informative than ``good" jump risk in forecasting future volatility. The volatility forecasting model proposed is able to capture many empirical stylized facts while still remaining parsimonious in terms of the number of parameters to be estimated.Type:journal articleJournal:EconometricsVolume:4Issue:1Scopus© Citations 54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance EstimationMotivated by the need of a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a covariance matrix estimate which is robust to both asynchronicity and microstructure noise, and positive-semidefinite by construction. We show the performance of the KEM estimator using extensive Monte Carlo simulations that mimic the liquidity and market microstructure characteristics of the S&P 500 universe as well as in an high-dimensional application on US stocks. KEM provides very accurate covariance matrix estimates and significantly outperforms alternative approaches recently introduced in the literature.Type:journal articleJournal:Journal of Applied EconometricsVolume:30Issue:3DOI:10.1002/jae.2378Scopus© Citations 33 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Lassoing the HAR model: A Model Selection Perspective on Realized Volatility DynamicsRealized volatility computed from high-frequency data is an important measure for many applications in finance and its dynamics have been widely investigated. Recent notable advances that perform well include the heterogeneous autoregressive (HAR) model which can approximate long memory, is very parsimonious, is easy to estimate, and features good out-of-sample performance. We prove that the least absolute shrinkage and selection operator (lasso) recovers the lags structure of the HAR model asymptotically if it is the true model, and we present Monte Carlo evidence in finite samples. The HAR model's lags structure is not fully in agreement with the one found using the lasso on real data. Moreover, we provide empirical evidence that there are two clear breaks in structure for most of the assets we consider. These results bring into question the appropriateness of the HAR model for realized volatility. Finally, in an out-of-sample analysis we show equal performance of the HAR model and the lasso approach.Type:journal articleJournal:Econometric ReviewsIssue:35