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Tuesday, 10 March, 2026

Michal Hakala: Modeling Conditional Distribution of Intraday Returns

Dissertation Committee:

Stanislav Anatolyev (CERGE-EI, chair)

Jozef Baruník (IES)

Veronika Selezneva (Université Paris Dauphine)

Defense Committee:

Sergey Slobodyan (CERGE-EI, chair)

Byeongju Jeong (CERGE-EI)

Miloš Kopa (Faculty of Mathematics and Physics, Charles University)

Referees:

Ilze Kalnina (North Carolina State University)

Richard Luger (Université Laval)

Meeting link:https://cerge-ei.webex.com/cerge-ei/j.php?MTID=m99f70bcb75158cbe7a3421feaaa13e12room 402

Meeting number: 2742 074 5915, Meeting password: 385621

Abstract:

Time-series modeling of conditional distributions of intraday returns is of great importance to financial professionals and academic researchers. This work contributes to a methodological and empirical body of knowledge on conditional distributions of intraday asset returns. In Chapter 1, we study distributional diurnal patterns. We propose a new semi-parametric modeling framework for capturing distributional diurnal patterns inspired by traditional seasonal adjustment methods compatible with common models in the literature. Capturing distributional diurnal patterns substantially improves forecast precision. In Chapter 2, we study clustered commonality in the stock market to improve intraday volatility forecasts. Using sectors, industries, and data-driven clusters, we extend the heterogeneous autoregressive (HAR) model with relevant groups or clusters of stocks chosen by regularization methods. In Chapter 3, we propose a model with a novel distribution based on Tukey's H transformation to study conditional kurtosis of intraday returns and spillovers of extreme shocks in the stock market. Empirical applications are based on intraday large-cap US-listed stock returns.

Full Text: "Modeling Conditional Distribution of Intraday Returns"