Papers

GARCHX-NoVaS: A Bootstrap-Based Approach of Forecasting for GARCHX Models

This paper proposes a new NoVaS prediction method based on the GARCHX model. Also, interesting real-data applications are given to test this new method and standard techniques.

Scalable Subsampling Inference for Deep Neural Networks

We propose a scalable subsampling method on estimating standard fully connected DNN. It turns out that the mean square error bound of estimation based on DNN to a target regression function can be improved under mild conditions. Moreover, it can run faster than training a single DNN on the whole dataset. In addition, we propose various methods to estimate the bias order of DNN, build confidence and prediction intervals based on DNN.

Deep Limit Model-free Prediction in Regression (Working)

One paper about a Model-free prediction in the regression context with DNN (simple and interesting).

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

This paper is an empirical study of GARCHX-NoVaS prediction method on stock volatility forecasting with climate-related information.

Bootstrap prediction inference of nonlinear autoregressive models

We construct bootstrap prediction intervals in the multi-step ahead prediction problem with parametric model estimator; in particular, we develop an asymptotically valid quantile prediction interval as well as a pertinent prediction interval for future values. To correct the undercoverage of prediction intervals with finite samples, we further employ predictive—as opposed to fitted—residuals in the bootstrap process.

Multi-Step-Ahead prediction intervals for nonparametric autoregressions via Bootstrap: consistency, debiasing, and pertinence

We consider a forward bootstrap approach with non-parametric model estimator to resolve the difficulty on predicting multi-step ahead time series data. We construct a quantile prediction interval that is asymptotically valid. Moreover, after taking a debiasing technique, we can build pertinent prediction intervals in which the estimation variability is captured. (This is my first paper with my advisor Professor Politis. Thanks for his guidance!)

A model-free approach to do long-term volatility forecasting and its variants

This paper proposes a new NoVaS prediction method based on the GARCH model. Compared to the existing NoVaS method which is motivated by ARCH model. We show the our new method is more robust and accurate.

Model-free time-aggregated predictions for econometric datasets

This paper applies a so-called Molde-free prediction method—NoVaS—to do volatility forecasting of financial series. We develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, we verify its great performance on long-term time aggregated predictions. (This is my first paper with Professor Karmakar. Thanks for his guidance!)