Stylized facts and trends of high frequency data in financial markets

2017 ◽  
Vol 7 (7) ◽  
pp. 303
Author(s):  
Haritika Arora
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Stylized Facts

scholarly journals COVID-19 Pandemic and Financial Contagion

2020 ◽  
Vol 13 (12) ◽  
pp. 309 ◽  
Author(s):  
Julien Chevallier
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
Value At Risk ◽  
Data Science ◽  
High Frequency Data ◽  
Financial Contagion ◽  
Frequency Data ◽  
Var Models ◽  
Wall Street ◽  
Original Contribution

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of models throughout our experiments: (i) the Susceptible-Infective-Removed (SIR) that predicts the infections’ peak on 2020-03-27; (ii) volatility (GARCH), correlation (DCC), and risk-management (Value-at-Risk (VaR)) models that relate how bears painted Wall Street red; and, (iii) data-science trees algorithms with forward prunning, mosaic plots, and Pythagorean forests that crunch the data on confirmed, deaths, and recovered Covid-19 cases and then tie them to high-frequency data for 31 stock markets.

scholarly journals Inference for time-varying lead–lag relationships from ultra-high-frequency data

2021 ◽  
Author(s):  
Yuta Koike
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Data ◽  
Time Varying ◽  
Frequency Data ◽  
Statistical Methodology ◽  
Microstructure Noise ◽  
New Approach ◽  
Proposed Model ◽  
Derivative Products

AbstractA new approach for modeling lead–lag relationships in high-frequency financial markets is proposed. The model accommodates non-synchronous trading and market microstructure noise as well as intraday variations of lead–lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented, as well. The methodology is illustrated by an empirical study to detect lead–lag relationships between the S&P 500 index and its two derivative products.

scholarly journals The Impact of Political Uncertainty on Asset Prices

2021 ◽  
Vol 21 (27) ◽  
Author(s):  
Joseph Hanna ◽  
Niels-Jakob Hansen ◽  
Margaux MacDonald
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Political Uncertainty ◽  
Differential Impact ◽  
Percentage Point Increase ◽  
Eu Membership ◽  
Point Increase ◽  
The Impact

How did expectations of the outcome of the United Kingdom's (UK) referendum on European Union (EU) membership in 2016 affect prices in financial markets? We study this using high frequency data from betting and financial markets. We find that a one percentage point increase in the probability of "Leave" result caused British stocks (FTSE All-Share) to decline by 0.004 percent, and the Pound to depreciate by 0.006 percent against the euro. We find negative and significant effects for most sub-sectors, and negative spill-overs to other EU member countries. We show that the differential impact across sectors and countries can be explained by differences in the trade exposures.

scholarly journals S&P 500 Index Price Spillovers around the COVID-19 Market Meltdown

2021 ◽  
Vol 14 (7) ◽  
pp. 330
Author(s):  
Camillo Lento ◽  
Nikola Gradojevic
Keyword(s):  
Exchange Rate ◽  
Financial Markets ◽  
Frequency Domain ◽  
High Frequency ◽  
Spillover Effects ◽  
High Frequency Data ◽  
Frequency Data ◽  
Data Set ◽  
Time Period ◽  
Price Movements

This paper explores price spillover effects around the COVID-19 pandemic market meltdown between the S&P 500 index, five other financial markets, and the VIX. Frequency domain causalities are estimated for the January–May 2020 time period on a high-frequency data set at five-minute intervals. The results reveal that price movements in the S&P 500 generally caused price movements in other financial markets before the market meltdown; however, a large number of bi-directional causalities emerged during the market meltdown. During the market recovery, S&P 500 price movements were more likely to be caused by other financial markets’ price movements. The VIX, exchange rate, and gold returns had the most prominent influence on the S&P 500 returns in the market recovery.

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