scholarly journals Robust Statistical Pearson Correlation Diagnostics for Bitcoin Exchange Rate with Trading Volume: An Analysis of High Frequency Data in High Volatility Environment

2017 ◽  
Vol 3 (12) ◽  
pp. 1135-1142 ◽  
Author(s):  
Nashirah Abu Bakar ◽  
Sofian Rosbi
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Trading Volume ◽  
Pearson Correlation ◽  
High Frequency Data ◽  
Frequency Data ◽  
High Volatility

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 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.

scholarly journals Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume

2021 ◽  
Vol 12 (1) ◽  
pp. 61-74
Author(s):  
Josip Arneric
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Missing Values ◽  
High Frequency Data ◽  
Seasonal Patterns ◽  
Time Varying ◽  
Frequency Data ◽  
Seasonal Component ◽  
Univariate Time Series ◽  
Over Time

The seasonal and trend decomposition of a univariate time-series based on Loess (STL) has several advantages over traditional methods. It deals with any periodicity length, enables seasonality change over time, allows missing values, and is robust to outliers. However, it does not handle trading day variation by default. This study offers how to deal with this drawback. By applying multiple STL decompositions of 15-minute trading volume observations, three seasonal patterns were discovered: hourly, daily, and monthly. The research objective was not only to discover if multi-seasonality exists in trading volume by employing high-frequency data but also to determine which seasonal component is most time-varying, and which seasonal components are the strongest or weakest when comparing the variation in the magnitude between them. The results indicate that hourly seasonality is the strongest, while daily seasonality changes the most. A better understanding of trading volume multiple patterns can be very helpful in improving the performance of trading algorithms.

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