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Flexible HAR Model for Realized Volatility

Prof Ostap Okhrin Technische Universitat Dresden

Date:28 September 2018, Friday

Location:S16-06-118, Seminar Room

Time:04:30pm - 05:30pm


The Heterogeneous Autoregressive (HAR) model is commonly used in modeling the dynamics of realized volatility. In this paper, we propose a flexible HAR(1,…,p) specification, employing the adaptive LASSO and its statistical inference theory to see whether the lag structure (1, 5, 22) implied from an economic point of view can be recovered by statistical methods. The model differs from Audrino and Knaus (2016) where the authors apply LASSO on the AR(p) model, which does not necessarily lead to a HAR model. Adaptive LASSO estimation and the subsequent hypothesis testing results fail to show strong evidence that such a fixed lag structure can be recovered by a flexible model. We also apply the group LASSO and related tests to check the validity of the classic HAR, which is rejected in most cases. The results justify our intention to use a flexible lag structure while still keeping the HAR frame. In terms of the out-of-sample forecasting, the proposed flexible specification works comparably to the benchmark HAR(1, 5, 22). Moreover, the time-varying model combinations show that when the market environment is not stable, the fixed lag structure (1, 5, 22) is not particularly accurate and effective. Ostap ORHRIN, Technische Universität Dresden.

The SEED seminar series is jointly organized by researchers fromNational University of Singapore, Zuse Institute Berlin, The Institute of Statistical Mathematics, Academia Sinica, University College London, Seoul National University and the Hong Kong University of Science and Technology. SEED stands for Statistics maschinElEarning Datascience. Motivated by the availability of big complex data and the fast development of new techniques in machine learning and data science, SEED aims to provide an online research platform for seminars focusing on important and timely interdisciplinary research topics on Statistics, Machine learning, Data Science, Mathematics, Operation Research, Computer Science, and Engineering. The online seminar series are co-hosted and organised by several research institutes in different countries. The mission is to exchange research ideas, educate young researchers, and promote international research and education collaborations.For more information, please visit the SEED website