arXiv research

The study compares econometric and deep learning models for forecasting COMEX copper futures volatility.

LSTM models struggle with volatility prediction due to financial complexities.

Survey on factor models and their applications in econometrics.

Foundation models improve on econometric benchmarks for forecasting volatility, but vary widely across models.

An econometric analysis of CRIX family indices.

NoLBERT avoids lookback and lookahead biases for better econometric inference.

Bayesian econometrics improves nowcasting during pandemics.

Paper examines two methods for FX market volatility modeling.

A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the rel…

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the machine learning l…

Sophisticated volatility models outperform naive portfolio strategies.

Paper compares econometric models with machine learning for energy forecasting.

Bayesian model predicts mid-price dynamics in financial markets.

ddml aids causal inference in econometrics with machine learning.

Research combines econometric, machine learning, and deep learning models for financial forecasting.

Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.

Econometric framework integrates heavy-tailed distributions with behavioral probability weighting for better asset pricing.

In the following paper, we analyse the ID$_3$-Price in the German Intraday Continuous electricity market using an econometric time series model. A multivariate approach is conducted for hourly and quarter-hourly products separately. We estimate the model using lasso and elastic net techniques and perform an out-of-samp…

A scalable method for econometric inference using machine learning for big data.

Unified treatment of CLTs for Lévy models across physics, finance, and econometrics.

New econometric results for financial duration models under varying tail behaviors.

The paper addresses fairness in machine learning models through structural econometrics, projecting indexes into null spaces to find fair solutions.

Regime-switching models, in particular Hidden Markov Models (HMMs) where the switching is driven by an unobservable Markov chain, are widely-used in financial applications, due to their tractability and good econometric properties. In this work we consider HMMs in continuous time with both constant and switching volati…

Study improves prediction of UK road accidents' severity using AI.

MARS model outperforms others in stock price prediction across sectors.

New method for adaptive estimation and inference in econometric models without knowing smoothness.

Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, includi…

This paper examines the time series properties of cryptocurrency assets, such as Bitcoin, using established econometric inference techniques, namely models of the GARCH family. The contribution of this study is twofold. I explore the time series properties of cryptocurrencies, a new type of financial asset on which the…

Paper develops robust econometric methods for staggered adoption studies.

Paper derives an error bound for stochastic LTI systems.

ReGEN-TAD detects anomalies in financial time series with interpretable models.

This paper gives a brief overview on the nonparametric techniques that are useful for financial econometric problems. The problems include estimation and inferences of instantaneous returns and volatility functions of time-homogeneous and time-dependent diffusion processes, and estimation of transition densities and st…

Paper provides conditions for reliable use of pre-trained embeddings in econometrics.

The Efficient Market Hypothesis has been a staple of economics research for decades. In particular, weak-form market efficiency -- the notion that past prices cannot predict future performance -- is strongly supported by econometric evidence. In contrast, machine learning algorithms implemented to predict stock price h…

Quantum reservoir computing improves volatility forecasting.

Study uses deep neural networks for inference in partially linear models with dependent data.

Hybrid GARCH-LSTM models predict covariance matrices better than GARCH alone.

Paper presents a dynamic tail risk protection strategy using ML and econometrics.

We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.

tempdisagg transforms low-frequency data into high-frequency estimates.

State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and epidemiology. In this paper we provide a fast approach for approximate Bayesian inference in SSMs using the tools of deep learning and variati…

Paper forecasts tax revenues in Bulgaria during pandemic.

New method solves quantile crossing problem in econometrics.

The paper introduces a new $σ$-LSTM cell for volatility forecasting using stylized facts.

Deep learning improves macroeconomic forecasting and risk assessment.

Dynamic econometric models improve trading signals in momentum strategies.

Enhanced multivariate GARCH model using LSTM for better volatility forecasting.

Deep learning models outperform classical methods in forecasting company fundamentals.