The paper analyzes and forecasts intraday electricity prices using econometric models.
problem Analyzing and forecasting the efficiency of the German Intraday Continuous electricity market.
method Multivariate econometric time series model with lasso and elastic net techniques.
result The model provides new insights into the ID 3 _3 3 -Price behavior and market efficiency. The study compares econometric and deep learning models for forecasting COMEX copper futures volatility.
problem Forecasting volatility of COMEX copper futures across different time intervals.
method Econometric models (GARCH, HAR) and deep learning models (RNN, LSTM, GRU) applied to daily and hourly data.
result Deep learning models outperform econometric models in hourly data, but HAR remains the best overall for daily data.
Discuss ML methods for economists, highlighting better performance in econometrics.
problem Applying ML methods to econometrics problems.
method Supervised and unsupervised learning methods, matrix completion, causal inference, optimal policy estimation.
result ML methods often outperform traditional econometric methods in specific econometrics problems.
LSTM models struggle with volatility prediction due to financial complexities.
problem Volatility prediction in financial markets is challenging due to various factors.
method Comparison of LSTM models with econometric models for volatility prediction.
result LSTM models do not outperform strong econometric models in volatility prediction.
Survey on factor models and their applications in econometrics.
problem Estimating low-rank structures in high-dimensional models.
method Low-rank recovery techniques for factor model estimation.
result New insights into factor model applications in econometrics.
Foundation models improve on econometric benchmarks for forecasting volatility, but vary widely across models.
problem Comparing pretrained time series foundation models to econometric benchmarks for volatility forecasting.
method Systematic comparison of nine zero-shot TSFMs against eight econometric specifications on 50 assets across 3 markets and 3 horizons.
result Tiny Time Mixers (TTM) is the only model that consistently beats the Log-HAR benchmark, but performance varies widely across models.
An econometric analysis of CRIX family indices.
problem Understanding the dynamics of CRIX family indices for pricing.
method Time-series econometric analysis using ARIMA and GARCH models.
result ARIMA(2,0,2)-t-GARCH(1,1) model captures volatility clustering and fat-tails.
NoLBERT avoids lookback and lookahead biases for better econometric inference.
problem Information leakage in language models affects econometric inference.
method Pretrained on text from 1976-1995, avoiding lookback and lookahead biases.
result NoLBERT outperforms domain-specific baselines and predicts higher profit growth.
Bayesian econometrics improves nowcasting during pandemics.
problem Improving nowcasting during extreme economic events like pandemics.
method Bayesian econometric methods using non-parametric mixed frequency VARs with additive regression trees.
result Significant improvements in nowcasting performance compared to linear models.
Paper examines two methods for FX market volatility modeling.
problem FX market volatility modeling problem.
method Classical econometric GCH and mathematical approaches (SSA, dynamical systems stability analysis).
result Both mathematical tools show promising results in 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…
Sophisticated volatility models outperform naive portfolio strategies.
problem Improving mean-variance portfolio performance over the naive 1/N strategy.
method Investigated various econometric and portfolio models across multiple datasets.
result Most models achieve higher Sharpe ratios and lower portfolio volatility than the naive rule.
Paper compares econometric models with machine learning for energy forecasting.
problem Tackles the trade-off between predictive accuracy and interpretability in energy markets.
method Integrates TVP-SVAR with copulas for forecasting energy--macro dynamics.
result Copula-enhanced econometric models provide interpretable insights while matching machine learning accuracy.
Bayesian model predicts mid-price dynamics in financial markets.
problem Challenges in predicting financial markets using traditional methods.
method Bayesian bilinear neural network with temporal attention.
result Feasibility and advantages of Bayesian deep-learning approach.
ddml aids causal inference in econometrics with machine learning.
problem Estimation of causal effects with endogenous variables and unknown functional forms.
method Double/Debiased Machine Learning (DDML) in Stata.
result Monte Carlo evidence supports using DDML with stacking for causal inference.
Research combines econometric, machine learning, and deep learning models for financial forecasting.
problem Improving financial time series forecasting accuracy.
method Hybrid models combining ARIMA, SVM, XGBoost, and LSTM.
result Effective hybrid models outperform individual components and the Buy&Hold strategy.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
problem Accurate short-term load forecasting for optimizing electrical sources and protecting energy.
method Uses SARIMA-GARCH model with T-student Distribution to forecast electric load.
result The proposed model outperforms the ARIMA model with Normal Distribution.
Econometric framework integrates heavy-tailed distributions with behavioral probability weighting for better asset pricing.
problem Underestimation of Value-at-Risk by traditional models in asset pricing.
method Developed an econometric framework combining heavy-tailed Student's t t t distributions with behavioral probability weighting. result Student's t t t specifications outperform Gaussian models in 88.4% of cases, reducing underestimation of Value-at-Risk by 16.5 percentage points. A scalable method for econometric inference using machine learning for big data.
problem Interpreting large, often black-box, economic data.
method Variational Bayesian Inference for time-varying parameter auto-regressive models.
result The model can handle large datasets and is scalable for big data.
Unified treatment of CLTs for Lévy models across physics, finance, and econometrics.
problem Understanding convergence of stochastic integrals in Lévy models.
method Unified weak convergence results for Skorokhod spaces J1 and M1.
result General principles apply to specific settings, yielding new insights.
New econometric results for financial duration models under varying tail behaviors.
problem Estimation and inference challenges in financial durations models with random event counts.
method Analysis of likelihood estimators for ACD models, focusing on tail behavior and stationarity.
result Asymptotic normality breaks down for tail indices smaller than one, leading to mixed Gaussian estimators with non-standard rates of convergence.
The paper addresses fairness in machine learning models through structural econometrics, projecting indexes into null spaces to find fair solutions.
problem Fairness concerns in machine learning, especially regarding disadvantaged groups.
method Model fairness as a linear operator, projecting indexes into null spaces to find fair solutions, balancing status quo and full fairness.
result Achieving approximate fairness by introducing a fairness penalty and balancing influences.
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…
Enhances binscatter method for better visualization and econometrics.
problem Incorrect conclusions from covariate adjustment in binscatter.
method Formalizes binscatter properties, introduces new tools for estimation and uncertainty quantification.
result Substantially different results from prior methods in applications.
Study improves prediction of UK road accidents' severity using AI.
problem Improving prediction of UK road traffic accident severity.
method Combination of machine learning, econometric, and statistical methods on historical data.
result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.
MARS model outperforms others in stock price prediction across sectors.
problem Developing accurate models for stock price prediction.
method Used time series, econometric, machine learning, and deep learning models on stock data.
result MARS model is the best performing model across IT, Banking, and Health sectors.
New method for adaptive estimation and inference in econometric models without knowing smoothness.
problem Adaptive estimation and inference in ill-posed linear inverse problems with unknown smoothness.
method Discrepancy principle-based framework for adaptive hyperparameter selection.
result Achieves optimal rates in weak and strong metrics for linear functionals.
The paper addresses misspecification in econometric models of discrete unobserved heterogeneity.
problem Misspecification in econometric models of discrete unobserved heterogeneity.
method Generalizing previous approaches to allow multiple latent variables, developing inference results for a k-means style estimator, and proposing information criteria for model selection.
result Over-fitting can be severe in k-means style estimators when the number of clusters is over-specified.
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.
problem Estimation challenges in event studies with staggered adoption.
method Design-first framework with exact probability limits, diagnostics, and orthogonal score constructions.
result Uniformly valid inference under restricted violations of parallel trends.
Paper derives an error bound for stochastic LTI systems.
problem Stochastic LTI systems with inputs in control engineering and econometrics.
method PAC-Bayesian-Like error bound derivation.
result Derived an error bound for stochastic LTI systems.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
Generative model predicts post-treatment outcomes in econometrics.
problem Estimating treatment impact on a single unit with a control sample.
method Generative learning approach, transforming data to Gaussian, using Fourier space Gaussian process analysis, combining Gaussian prior with data likelihood.
result Synthetic prediction minimizes error variance, unique minimum variance counterfactual.
Paper provides conditions for reliable use of pre-trained embeddings in econometrics.
problem Uncertainty in using pre-trained embeddings for econometric tasks.
method Derives sufficient conditions and convergence rates for machine learning models with pre-trained embeddings.
result Establishes theoretical foundations for reliable use of pre-trained embeddings in econometrics.
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…
Quantum reservoir computing improves volatility forecasting.
problem Forecasting realized volatility in finance.
method Quantum reservoir computing with Ising Hamiltonian and feature selection.
result Quantum reservoir computing outperforms benchmarks in volatility forecasting.
Study uses deep neural networks for inference in partially linear models with dependent data.
problem Inference in partially linear models with dependent data.
method First stage deep neural network (DNN) estimation followed by n \sqrt{n} n -consistent and asymptotically normal estimator. result The DNN-estimated finite dimensional parameter achieves n \sqrt{n} n -consistency and asymptotic normality. A new pricing model from game theory fits financial data well.
problem Financial models lack economic justification and randomness assumptions.
method CMMV pricing model based on game theory and information asymmetry.
result The CMMV model predicts option prices and volatility surface well.
Hybrid GARCH-LSTM models predict covariance matrices better than GARCH alone.
problem Predicting covariance matrices of high-dimensional asset returns.
method Combining GARCH processes with neural networks to forecast volatilities and correlations.
result The hybrid model outperforms both equally weighted portfolios and univariate GARCH models.
This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.
problem Accurate prediction of foreign exchange rates for investment purposes.
method Multivariate time series analysis using Vector Auto Regression, Support Vector Machine, and Recurrent Neural Networks.
result Contemporary machine/deep learning techniques outperform traditional econometric methods in forecasting foreign exchange rates.
Paper presents a dynamic tail risk protection strategy using ML and econometrics.
problem Tail risk protection in finance with solid mathematical and statistical tools.
method Dynamic tail risk protection strategy using weak classifiers (parametric and non-parametric) to estimate exceedance probability and derive trading signals.
result Ensemble classifier improves generalization and trading performance.
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.
Study confirms weak-form market efficiency using machine learning on US stock data.
problem Validating weak-form market efficiency using machine learning.
method Conducted econometric tests and implemented algorithmic trading with five machine learning algorithms.
result No predictive power found in any machine learning model, reinforcing weak-form market efficiency.
tempdisagg transforms low-frequency data into high-frequency estimates.
problem Transforming low-frequency data into high-frequency estimates.
method Uses econometric techniques including Chow-Lin, Denton, Litterman, Fernandez, and uniform interpolation.
result Transforms low-frequency aggregates into consistent, 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.
problem Forecasting tax revenues during pandemic.
method Model based on IMF recommendations, using 1995-2019 data.
result Pandemic negatively impacts tax revenues, but econometrics can still produce forecasts.
New method solves quantile crossing problem in econometrics.
problem Quantile crossing problem in quantile regression.
method Flexible check function approach.
result Eliminates or greatly reduces quantile crossing problem.