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 Evidence of Information Asymmetry and Herding Behaviour – The Swiss Franc Unpegging Event in Perspective

2020 ◽  
Vol 19 (3) ◽  
pp. 59-77
Author(s):  
Shruti Garg
Keyword(s):  
Information Asymmetry ◽  
Short Duration ◽  
High Frequency ◽  
Swiss Franc ◽  
High Frequency Data ◽  
Frequency Data ◽  
Indian Market ◽  
Causality Test ◽  
Financial Events ◽  
The Impact

The paper aims to find the impact of financial events that occurred in one country on another. Taking the case of the Swiss Franc Unpegging of 2015 in Switzerland, the paper observes its impact on the Indian economy. This is done by studying the information asymmetry and herding behaviour in Indian market on the day of the event. The study uses two sets of data, (i) high frequency data and (ii) 3 years index data of both countries. The Ganger Causality test has been conducted to study the cause and effect relationship between the economies, which helps determine the impact on any of the countries. The study found that herding behaviour and information asymmetry in Indian market are now linked to each other in such a way that the country is affected even if the event has not occurred in the economy itself, however, only for a short duration of time. There also seems to be a huge gap between information available amongst all investors.

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 Pairs trading with the persistence-based decomposition model

2020 ◽  
Vol 20 (2) ◽  
pp. 151
Author(s):  
Jonas Rende
Keyword(s):  
High Frequency ◽  
Scientific Community ◽  
Large Scale ◽  
Model Fitting ◽  
Short Selling ◽  
High Frequency Data ◽  
Frequency Data ◽  
Pairs Trading ◽  
Risk Return ◽  
The Impact

Recently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical evidence that the model adopts well to noisy high-frequency data in terms of model fitting and prediction. We put the PBD model to test on a large-scale high-frequency pairs trading application, using SP 500 minute-by-minute data from 1998 to 2016. After accounting for execution limitations (waiting rule, volume constraints, and short-selling fees) the PBD model yields statistically significant and economically meaningful annual returns after transaction costs of 9.16 percent. These returns can only partially be explained by the exposure to common risk. In addition, the model is superior in terms of risk-return metrics. The model performs very well in bear markets. We quantify the impact of execution limitations on risk and return measures by relaxing backtesting restrictions step-by-step. If no restrictions are imposed, we find annual returns after costs of 138.6 percent.

Stock Price Jumps and News Sentiment: A Case of Investor Overreaction

2018 ◽  
Vol 11 (2) ◽  
pp. 20-37
Author(s):  
Vinay Kumar Apparaju ◽  
Ashwani Kumar ◽  
Ritu Yadav
Keyword(s):  
High Frequency ◽  
Stock Price ◽  
Strong Form ◽  
High Frequency Data ◽  
Options Market ◽  
Frequency Data ◽  
Innovative Method ◽  
Investor Behaviour ◽  
News Sentiment ◽  
The Impact

The research paper develops an understanding on how news based sentiment capture investor behaviour reflected in price jumps in stock markets. It compares the impact on two models of stock price jumps; the non-parametric model proposed by BNS and the wavelet based method. The study is also a perspective on the semi strong form of market efficiencyUsing the high frequency data from the stock and options market along with the actual high frequency news data from Bloomberg, the two alternative methodologies of jumps have been tested. In addition, options trades have been simulated to see whether profits can be earned from the news sentiment captured by jumps.Methodologically, jumps based on wavelets were found to be better related  with the news sentiment compared to the BNS method. Also,   the news sentiment based jumps were found to present opportunities in the simulated trades that could be exploited for earning profits suggesting that investors overreact.The paper uses an innovative method for computation of the news based sentiment. To the best of our knowledge, the paper is the first to evaluate jumps and news sentiment using the actual news data. A perspective on the semi strong form of market efficiency is presented, that too by departing from the event study based models. 

scholarly journals Forecasting USD-BRL Currency Rate Volatility using Realized and Implied Volatilities Data

2018 ◽  
Vol 48 (4) ◽  
pp. 687-719
Author(s):  
Carlos Heitor Campani ◽  
Assis Gustavo da Silva Durães
Keyword(s):  
High Frequency ◽  
Implied Volatility ◽  
Statistical Significance ◽  
Relevant Information ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Exogenous Variables ◽  
Foreign Exchange Volatility ◽  
The Impact

Abstract This article assesses the impact of exogenous variables in GARCH models, when applied to volatility forecasts for the Brazilian USD-BRL currency market. As exogenous variables, we used the realized variance, based on high frequency data, and the FXVol index, based on market implied volatility data. This is the first study to use the FXVol index and to investigate its effects on Brazilian foreign exchange volatility. The results indicate statistical significance of the superiority of the extended models when predicting volatility. We conclude that high frequency data and market implied volatility contain relevant information with respect to USD-BRL currency volatility. These find ings are relevant for hedgers, speculators and practitioners in general.

Sign in / Sign up

Export Citation Format

Share Document