Trading Activity and Macroeconomic Announcements in High-Frequency Exchange Rate Data

2008 ◽  
Vol 6 (2-3) ◽  
pp. 589-596 ◽  
Author(s):  
Alain P. Chaboud ◽  
Sergey V. Chernenko ◽  
Jonathan H. Wright
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Rate Data ◽  
Trading Activity ◽  
Macroeconomic Announcements

scholarly journals Searching for histogram patterns in USD/ZAR foreign exchange financial time series

2009 ◽  
Vol 6 (4) ◽  
pp. 575-584
Author(s):  
JH Van Rooyen
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
High Frequency ◽  
Financial Risk ◽  
Pattern Analysis ◽  
Financial Time Series ◽  
Rate Data ◽  
Data Set ◽  
Frequency Distributions ◽  
Financial Time

This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from tick high frequency currency exchange rates over a period of one week. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behaviour of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study, which is a follow up of research by Van ZylBulitta, VH, Otte, R and Van Rooyen, JH, special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states and the repetition of histogram patterns

scholarly journals MARKOV PROPERTIES OF HIGH FREQUENCY EXCHANGE RATE DATA

2000 ◽  
Vol 03 (03) ◽  
pp. 415-416 ◽  
Author(s):  
C. RENNER ◽  
J. PEINKE ◽  
R. FRIEDRICH
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Rate Data ◽  
Markov Properties

Testing for unit roots with very high frequency spot exchange rate data

1993 ◽  
Vol 15 (3) ◽  
pp. 423-438 ◽  
Author(s):  
Charles A.E. Goodhart ◽  
Patrick C. McMahon ◽  
Yerima L. Ngama
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Unit Roots ◽  
Rate Data ◽  
Very High Frequency ◽  
Spot Exchange Rate ◽  
Very High

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 Application of Generalized Space Time Autoregressive Model on Farmer Exchange Rate Data in Three Provinces of The Sumatera Island

2020 ◽  
Vol 21 (2) ◽  
pp. 97
Author(s):  
Fadhilatul Nida Aryani ◽  
Sri Sulistijowati Handajani ◽  
Etik Zukhronah
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Agricultural Sector ◽  
Space Time ◽  
Rate Data ◽  
Previous Time ◽  
West Sumatra ◽  
Cross Correlations ◽  
The Exchange Rate ◽  
Inverse Distance

The agricultural sector has a big role in the development of the Gross Regional Domestic Product (GDP). Therefore the agricultural sector is very important. Besides the agricultural sector, the farmer's welfare also needs to be considered because the agricultural sector will be good if the welfare of farmers is good also. In measuring the level of farmers' welfare, the method used is the farmer's exchange rate. The farmer's exchange rate has a location relationship and a previous time relationship. The Generalized Space-Time Autoregressive (GSTAR) model is a good method of forecasting data that contains time series and location relationships by assuming that the data has heterogeneous characteristics. The purpose of this study is to model the farmer exchange rate data with GSTAR using normalization of cross-correlations weighting and inverse distance in three provinces namely West Sumatra, Bengkulu and Jambi Provinces. Based on data analysis, the best GSTAR model obtained by using the best weighting with the model is GSTAR (11) − I(1) using normalization of cross-correlations because the assumption of normal white noise and multivariate are fulfilled with an RMSE value of 1.097775. The best GSTAR model explains that the exchange rate of West Sumatra farmers is only the previous time, Bengkulu farmers' exchange rate is the previous time and is the exchange rates of farmers of West Sumatra and Jambi, whereas for the exchange rate of farmers of Jambi is the exchange rates of farmers of Bengkulu and West Sumatra and influenced by previous times.Keywords: GSTAR, RMSE, farmers exchange rate, normalization of cross-correlations, inverse distance.

scholarly journals Macroeconomic News, Order Flows, and Exchange Rates

2008 ◽  
Vol 43 (2) ◽  
pp. 467-488 ◽  
Author(s):  
Ryan Love ◽  
Richard Payne
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Public Information ◽  
Relevant Information ◽  
Order Flow ◽  
Macroeconomic News ◽  
Market Participants ◽  
Macroeconomic Announcements ◽  
Information Assimilation ◽  
Micro Level

AbstractIn textbook models of exchange rate determination, the news contained in public information announcements is directly impounded into prices with there being no role for trading in this process of information assimilation. This paper directly tests this theoretical result using transaction level exchange rate return and trading data and a sample of scheduled macroeconomic announcements. The main result of the paper is that even information that is publicly and simultaneously released to all market participants is partially impounded into prices via the key micro level price determinant—order flow. We quantify the role that order flow plays and find that approximately one third of price-relevant information is incorporated via the trading process.

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