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.
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 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.
Pricing and hedging rainbow options using Bayesian MS-VAR process.
problem Pricing and hedging rainbow options under varying economic conditions.
method Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process to model regime-switching economic variables.
result Model provides a simpler and more economic variable-dependent approach for rainbow options pricing and hedging.
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…
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.
Deep learning improves macroeconomic forecasting and risk assessment.
problem Improving accuracy in macroeconomic forecasting and sovereign risk assessment.
method Nowcasting and forecasting using deep learning techniques.
result Deep learning methods outperform traditional econometric techniques in out-of-sample performance.
Variational Inference shows promise for Bayesian GARCH model estimation.
problem Bayesian estimation of GARCH-family models using Monte Carlo sampling.
method Variational Inference as an alternative to Monte Carlo sampling.
result Variational Inference is a reliable and competitive method for Bayesian learning in GARCH-like models.
This paper benchmarks econometric and machine learning methods in nowcasting GDP growth.
problem Lack of comprehensive comparison of nowcasting methodologies.
method Examines 12 methodologies including traditional econometrics and machine learning.
result LSTM and BVAR are the top two performing methodologies.
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…
In the econometrics of financial time series, it is customary to take some parametric model for the data, and then estimate the parameters from historical data. This approach suffers from several problems. Firstly, how is estimation error to be quantified, and then taken into account when making statements about the fu…
Paper presents a new probabilistic approach for high-dimensional quantile prediction.
problem High-dimensional quantile prediction challenges in robust statistical methods.
method Pseudo-Bayesian framework with scaled Student-t prior and Langevin Monte Carlo.
result Demonstrates strong theoretical guarantees and competitive performance in simulations and real-world data.
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.
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.
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.
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…
Paper proposes efficient methods for forecasting with large datasets.
problem Forecasting with large, high-dimensional economic data sets.
method Bayesian hierarchical priors, factor graphs, message passing algorithms, Generalized Approximate Message Passing (GAMP).
result The proposed methods outperform traditional approaches in forecasting U.S. price inflation.
Regularization and Bayesian methods for system identification have been repopularized in the recent years, and proved to be competitive w.r.t. classical parametric approaches. In this paper we shall make an attempt to illustrate how the use of regularization in system identification has evolved over the years, starting…
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.
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.
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.
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.
In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics. We propose a novel parametrization that characterizes any collection of non-crossing quantile planes over arbitrarily shaped convex predictor domains in a…
The statistical description and modeling of volatility plays a prominent role in econometrics, risk management and finance. GARCH and stochastic volatility models have been extensively studied and are routinely fitted to market data, albeit providing a phenomenological description only. In contrast, the field of econop…
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.
Enhanced volatility model using LSTM and realized volatility.
problem Volatility modeling in financial markets.
method Combining deep learning (LSTM) and realized volatility measures in a Bayesian framework.
result Superior predictive performance compared to benchmark models.
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.
We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space mod…
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.
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundam…
Unified framework improves option pricing accuracy and stability.
problem Combining structured knowledge with data for better financial modeling.
method Structured-Knowledge-Informed Neural Networks (SKINNs) that embed theoretical insights into neural networks.
result SKINNs improve out-of-sample valuation and hedging performance in financial applications.
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 distributions with behavioral probability weighting. result Student's t specifications outperform Gaussian models in 88.4% of cases, reducing underestimation of Value-at-Risk by 16.5 percentage points. 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.
Researchers develop a new SMC sampler for Wishart processes to improve dynamic covariance inference.
problem Challenging inference of dynamic covariance in various scientific fields.
method Introduce Sequential Monte Carlo (SMC) sampler for the Wishart process.
result SMC sampling provides more robust estimates and out-of-sample predictions of dynamic covariance.
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.
We present a set of models relevant for predicting various aspects of intra-day trading volume for equities and showcase them as an ensemble that projects volume in unison. We introduce econometric methods for predicting total and remaining daily volume, intra-day volume profile (u-curve), close auction volume and spec…
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 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.
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.
Study finds economic data may not be as sparse as previously thought.
problem Modeling economic relations with many variables and prior sensitivity issues.
method Bayesian approach with Spike-and-Slab prior to evaluate variable selection and shrinkage.
result Prior distribution affects detection of sparsity patterns in economic data.
The accurate prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the variances. Moreover, function parameters are usually learned using maximum likelihood, which can lead to overfitt…
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.
In the following paper, we analyse the ID3-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…
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.
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.
Bayesian DL model improves DCMs for better predictive and inferential performance.
problem Limited interpretability and predictive underperformance of traditional DCMs.
method Integrates deep learning with approximate Bayesian inference (SGLD).
result Improves predictive and inferential metrics in discrete choice models.
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.
In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a ti…