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Jo­nas Stri­aukas

Tenure Track Assistant Professor

Subjects
Macroeconomics Statistics Investment

Primary research areas

High-dimensional statistics and econometrics

My research develops new high-dimensional statistical methods for economic and financial data. I focus on procedures for estimation, model selection, inference, and multiple testing procedures that accommodate large panels with cross-sectional and time series dependence, and heavy-tailed outcomes – salient features of modern empirical applications in economics and finance.

Nowcasting methods for economic activity

In my empirical research, I develop nowcasting methods that combine MIDAS models with machine learning techniques to measure economic activity in real time. I design estimation and inference procedures that handle mixed-frequency data and data revisions, and remain robust to serial and cross-sectional dependence and heavy-tailed data.

Methods for asset pricing and portfolio management

In my empirical research, I study asset pricing and portfolio management by designing tests for risk premia and optimal portfolios, leveraging high-frequency and alternative data (such as newspaper articles). I develop robust allocation and rebalancing rules that account for sequential multiple testing.

Statistical and Machine Learning Methods for Economic Data

In my research, I develop statistical and machine learning methods for high-dimensional, heavy-tailed, and dependent data, with applications in economics and finance. My work focuses on transforming complex, noisy datasets into reliable, real-time insights for decision-making. More recently, my research has focused on two areas: developing testing procedures for tail (i.e., extreme) outcomes, and developing sequential global testing frameworks that provide rigorous statistical guarantees.

In macroeconomic applications, I build nowcasting tools that use mixed-frequency (MIDAS) and machine learning techniques to track economic activity in real time. These methods are robust to publication lags, data revisions, and structural breaks—helping central banks and firms respond more quickly to sudden shifts in the economy.

In finance, I design tests for risk premia and implementable portfolio strategies using high-frequency market and alternative data (such as news articles).

My approaches account for realistic trading constraints, high dimensionality, and multiple testing challenges. I promote open and reproducible research and teach evidence-based analytics, statistics, and machine learning. I also collaborate with Euro area institutions to improve the speed, fairness, and transparency of decisions under uncertainty.

Outside activities

Trainee, National Bank of Belgium., 15/06/2022–28/09/2022

Machine Learning Based Nowcasting Model for National Bank of Belgium and Euro Area