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Liunice649's starred repositories

dccmidas

:exclamation: This is a read-only mirror of the CRAN R package repository. dccmidas — DCC Models with GARCH and GARCH-MIDAS Specifications in the Univariate Step, RiskMetrics, Moving Covariance and Scalar and Diagonal BEKK Models

Language:R700

StockPredictionRNN

High Frequency Trading Price Prediction using LSTM Recursive Neural Networks

Language:PythonMIT68200

Quantile

Robust neural networks based on nonlinear regression quantiles

Language:PythonMIT300

MQBoost

Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost

Language:Python1000

Multi-Sources-Quantile-Regression-Neural-Network-in-QWIM

This project presents the application of a MS-QRNN model designed to estimate Value at Risk accurately by integrating both numerical financial time-series data and textual data. The model incorporates NLP techniques, including FinBERT for textual analysis, and Neural Network architectures to predict the quantiles of asset return distributions.

Language:Jupyter Notebook800

Censored_Quantile_Regression_NN

NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'

Language:Jupyter NotebookMIT1100

mlrMBO

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Language:RNOASSERTION18700

AdpQMLE

Adaptive Quasi Maximum Likelihood Estimation of GARCH models

Language:RMIT700

PAsso

An implementation of the unified framework for assessing Partial Association between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the American Statistical Association). This package provides a set of tools to quantify, visualize, and test partial associations between multiple ordinal variables.

Language:R700

Quasi_Maximum_Likelihood_Estimation_of_the_GJR-GARCH_Model_Using_Matlab

Quasi Maximum Likelihood Estimation of the GJR-GARCH Model with Matlab

Language:Matlab200

Quasi-maximum-likelihood-estimation-QMLE-of-spatial-dynamic-panel-data

This package implements quasi-maximum likelihood estimation (QMLE) of spatial dynamic panel data based on paper by Lee & Yu (2011): https://www.sciencedirect.com/science/article/pii/S0304407616302147

Language:Jupyter NotebookMIT400

MASTERS_THESIS

Final thesis is added in the main branch, while the final versions of the different models related to the different applications are present in the sub-branches. Implied_vola relates to At-the-Money Implied volatility curve corrections. GARCH_analysis relates to obtaining VaR for large portfolios using autoencoders. RE_analysis performs a reconstruction analysis when changing the distributional assumptions of the underlying variational autoencoder.

Language:Python300

predicting-the-pound

MSc Finance dissertation project at Newcastle University. This project focused on forecasting the volatility of exchange rates involving the Great British Pound using EWMA, GARCH-type and Implied Volatility models.

Language:Python400

comparison-between-GARCH-type-models

The project is advised by Professor Robert Engle in his FINANCIAL ECONOMETRICS PhD course. I made comparison between the performance of different GARCH-type models, including GARCH, EGARCH, TGARCH and GJRGARCH, when forecasting implied volatility.

Language:R900

rt_regression_huber_regressor_sklearn

This repository is a dockerized implementation of the Huber regressor. It is implemented in flexible way that it can be used with any regression dataset with the use of JSON-formatted data schema file.

Language:PythonMIT100