Pinned Repositories
DEDA_Class_2019WS
DEDA class 2019 winter semester
DuesseldorfAirport
hf.econometrics
Companion to publication "Understanding Jumps in High Frequency Digital Asset Markets". Contains scalable implementations of Lee / Mykland (2012), Ait-Sahalia / Jacod (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data
icc.isvm
This library serves as a companion to the publication "Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing". However it can also be used independently for clustering high dimensional datasets and fitting an implied stochastic volatility model.
Jump_tests
Collection of code for detection and modeling of jumps (WIP)
JumpDetectR
Scalable implementation of Lee / Mykland (2012), Ait-Sahalia / Jacod (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data
media_server
ml_case_study
Machine Learning case study including an exploratory data analysis and fitting a Decision Tree, Random Forest, and XGBoost Model. Interactive notebook with outputs and visualizations: https://yaldan.github.io/ml_case_study/
plot_crypto_ts
SFM_Class_2019WS
Project repository for SFM1 class in semester 2019-2020
YalDan's Repositories
YalDan/hf.econometrics
Companion to publication "Understanding Jumps in High Frequency Digital Asset Markets". Contains scalable implementations of Lee / Mykland (2012), Ait-Sahalia / Jacod (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data
YalDan/JumpDetectR
Scalable implementation of Lee / Mykland (2012), Ait-Sahalia / Jacod (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data
YalDan/icc.isvm
This library serves as a companion to the publication "Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing". However it can also be used independently for clustering high dimensional datasets and fitting an implied stochastic volatility model.
YalDan/Jump_tests
Collection of code for detection and modeling of jumps (WIP)
YalDan/ml_case_study
Machine Learning case study including an exploratory data analysis and fitting a Decision Tree, Random Forest, and XGBoost Model. Interactive notebook with outputs and visualizations: https://yaldan.github.io/ml_case_study/
YalDan/DEDA_Class_2019WS
DEDA class 2019 winter semester
YalDan/DuesseldorfAirport
YalDan/media_server
YalDan/plot_crypto_ts
YalDan/SFM_Class_2019WS
Project repository for SFM1 class in semester 2019-2020