Scalable implementation of Lee / Mykland (2012) and Ait-Sahalia / Jacod (2012) Jump tests for noisy high frequency data

Related tags

Deep Learningfinancecryptocurrencyeconometricsnoisequantitative-financefinancial-analysisjumpvolatilitymicrostructurecontagionstatistical-testr-project
Overview

Visit QuantNet

Visit QuantNet JumpDetectR Visit QuantNet 2.0

Name of QuantLet : JumpDetectR

Published in : 'To be published as "Jump dynamics in high frequency crypto markets"'

Description : 'Scalable implementation of Lee / Mykland (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data'

Keywords : Jumps, jump test, high frequency, time series, Ait-Sahalia, Jacod, Lee, Mykland, stochastic processes, cryptocurrencies, cryptocurrency, crypto, spectrogram, microstructure, market microstructure noise, contagion, shocks

See also : 'Lee, S.S. and Mykland, P.A. (2012) Jumps in Equilibrium Prices and Market Microstructure Noise; Ait-Sahalia, Y. and Jacod, J., Jia Li (2012) Testing for jumps in noisy high frequency data'

Authors : Danial Florian Saef, Odett Nagy

Submitted : May 7 2021 by Danial Saef

Picture1

Picture2

Picture3

Picture4

Picture5

Picture6

Picture7

Picture8

Picture9

Picture10

Picture11

Picture12

R Code

## install and load packages ##
libraries = c("data.table")
lapply(libraries, function(x) if (!(x %in% installed.packages())) {install.packages(x)} )
invisible(lapply(libraries, library, quietly = TRUE, character.only = TRUE))
## ##

#### settings ####
Sys.setenv(LANG = "en") # set environment language to English
Sys.setlocale("LC_TIME", "en_US.UTF-8") # set timestamp language to English
## ##

#### load functions #####
source("./functions/make_return_file.R", echo = F)
source("./functions/LM_JumpTest_2012.R", echo = F)
source("./functions/AJ_JumpTest_2012.R", echo = F)
source("./functions/lapply_jump_test.R", echo = F)
source("./functions/AJL_Jump_Test_2012_functions.R", echo = F)
source("./functions/AJL_Jump_Test_2012.R", echo = F)
source("./functions/jacod_preaveraging.R", echo = F)
source("./functions/AJ_09_variation.R", echo = F)
source("./functions/split_by_id.R", echo = F)
source("./functions/remove_bounceback.R", echo = F)
#### ##


### load aggregate dataset ###
DT_agg_sub <- fread("./data/raw/DT_agg_sub.csv")
## ##

#### evaluate by id ####
## split data.table ##
DT_split_noimpute <- split_by_id(DT_agg_sub, IMPUTATION = FALSE)
DT_split_impute <- split_by_id(DT_agg_sub, IMPUTATION = TRUE)
DT_agg_split_noimpute <- rbindlist(DT_split_noimpute)
DT_agg_split_impute <- rbindlist(DT_split_impute)

## get LM result ##
DT_LM_result_id <- jump_test(DT_split_noimpute, which_test = "LM_JumpTest")

## get AJL result ##
DT_AJL_result_id <- jump_test(DT_split_impute, which_test = "AJL_JumpTest")

fwrite(DT_LM_result_id, file = "./data/JumpTestResult/DT_LM_result_id.csv")
fwrite(DT_AJL_result_id, file = "./data/JumpTestResult/DT_AJL_result_id.csv")
## ##

automatically created on 2021-05-17

Owner
LvB
QuantNet Tokens for science
LvB
GitHub Repository
Face Recognition Attendance Project

Face-Recognition-Attendance-Project In This Project You will learn how to mark attendance using face recognition, Hello Guys This is Gautam Kumar, Thi

Gautam Kumar 1 Dec 03, 2022
Minimal PyTorch implementation of YOLOv3

A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation.

Erik Linder-Norén 6.9k Dec 29, 2022
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022

Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr

Jeongwhan Choi 55 Dec 28, 2022
Code for approximate graph reduction techniques for cardinality-based DSFM, from paper

SparseCard Code for approximate graph reduction techniques for cardinality-based DSFM, from paper "Approximate Decomposable Submodular Function Minimi

Nate Veldt 1 Nov 25, 2022
The-Secret-Sharing-Schemes - This interactive script demonstrates the Secret Sharing Schemes algorithm

The-Secret-Sharing-Schemes This interactive script demonstrates the Secret Shari

Nishaant Goswamy 1 Jan 02, 2022
Auditing Black-Box Prediction Models for Data Minimization Compliance

Data-Minimization-Auditor An auditing tool for model-instability based data minimization that is introduced in "Auditing Black-Box Prediction Models f

Bashir Rastegarpanah 2 Mar 24, 2022
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

Memory-Efficient Multi-Level In-Situ Generation (MLG) By Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen and David Z. Pan

Jiaqi Gu 2 Jan 04, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
Face Transformer for Recognition

Face-Transformer This is the code of Face Transformer for Recognition (https://arxiv.org/abs/2103.14803v2). Recently there has been great interests of

Zhong Yaoyao 153 Nov 30, 2022
bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Code Submission for: Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks Run with docker To build a docker environment, chan

0 Dec 09, 2021
The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

cv516Buaa 9 Nov 07, 2022
Code and Datasets from the paper "Self-supervised contrastive learning for volcanic unrest detection from InSAR data"

Code and Datasets from the paper "Self-supervised contrastive learning for volcanic unrest detection from InSAR data" You can download the pretrained

Bountos Nikos 3 May 07, 2022
Asynchronous Advantage Actor-Critic in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

Reiji Hatsugai 38 Dec 12, 2022
Sequential model-based optimization with a `scipy.optimize` interface

Scikit-Optimize Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements

Scikit-Optimize 2.5k Jan 04, 2023
TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1).

M1-tensorflow-benchmark TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1). I was initially testing if Tens

particle 2 Jan 05, 2022
Cross-Document Coreference Resolution

Cross-Document Coreference Resolution This repository contains code and models for end-to-end cross-document coreference resolution, as decribed in ou

Arie Cattan 29 Nov 28, 2022
Direct design of biquad filter cascades with deep learning by sampling random polynomials.

IIRNet Direct design of biquad filter cascades with deep learning by sampling random polynomials. Usage git clone https://github.com/csteinmetz1/IIRNe

Christian J. Steinmetz 55 Nov 02, 2022
A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

Jack Turner 12 Nov 05, 2022
A Survey on Deep Learning Technique for Video Segmentation

A Survey on Deep Learning Technique for Video Segmentation A Survey on Deep Learning Technique for Video Segmentation Wenguan Wang, Tianfei Zhou, Fati

Tianfei Zhou 112 Dec 12, 2022
A simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

This is a simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

crispengari 3 Jan 08, 2022