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.

scholarly journals ESTIMATING THE FRACTAL DIMENSION OF THE S&P 500 INDEX USING WAVELET ANALYSIS

2004 ◽  
Vol 07 (05) ◽  
pp. 615-643 ◽  
Author(s):  
ERHAN BAYRAKTAR ◽  
H. VINCENT POOR ◽  
K. RONNIE SIRCAR
Keyword(s):  
Fractal Dimension ◽  
Computational Complexity ◽  
Wavelet Analysis ◽  
High Frequency ◽  
High Frequency Data ◽  
Hurst Parameter ◽  
Frequency Data ◽  
Data Set ◽  
Time Period ◽  
Data Segment

S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989–May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator using the log-scale spectrum is employed. The estimator is asymptotically Gaussian and the variance of the estimate that is obtained from a data segment of N points is of order [Formula: see text]. Wavelet analysis is tailor-made for the high frequency data set, since it has low computational complexity due to the pyramidal algorithm for computing the detail coefficients. This estimator is robust to additive non-stationarities, and here it is shown to exhibit some degree of robustness to multiplicative non-stationarities, such as seasonalities and volatility persistence, as well. This analysis suggests that the market became more efficient in the period 1997–2000.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data

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 Keynes and the dollar in 1933: the gold-buying program and exchange rate gyrations

2017 ◽  
Vol 24 (3) ◽  
pp. 209-238 ◽  
Author(s):  
Sebastian Edwards
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Gold Standard ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Open Letter ◽  
The Ideal

In December 1933, John Maynard Keyes published an open letter to President Roosevelt, where he wrote: ‘The recent gyrations of the dollar have looked to me more like a gold standard on the booze than the ideal managed currency of my dreams.’ This was a criticism of the ‘gold-buying program’ launched in October 1933. In this article I use high-frequency data on the dollar–pound and dollar–franc exchange rates to investigate whether the gyrations of the dollar were unusually high in late 1933. My results show that although volatility was pronounced, it was not higher than during some other periods after 1921. Moreover, dollar volatility began to subside towards the end of the period alluded to by Keynes.

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