Sample records for eurodollars
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Comparison of field theory models of interest rates with market data
NASA Astrophysics Data System (ADS)
Baaquie, Belal E.; Srikant, Marakani
2004-03-01
We calibrate and test various variants of field theory models of the interest rate with data from Eurodollar futures. Models based on psychological factors are seen to provide the best fit to the market. We make a model independent determination of the volatility function of the forward rates from market data.
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Information Technology Revolution in the Republic of Korea: Rise of the Knowledge-Based Society
2005-08-01
additional stock sales to the domestic investors, the sale of KT ADRs on Wall Street in 1999 and 2001, placements of Eurodollar bonds with warrants...to come. Trends in Production, Domestic Sales , and Imports/Exports in IT Industry Traditionally, the IT industry in Korea has been...distance, and 340,800 international telephone service subscribers.9 Their sales and profits continue to decline at 7.7 percent annually, despite
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Market inefficiency identified by both single and multiple currency trends
NASA Astrophysics Data System (ADS)
Tokár, T.; Horváth, D.
2012-11-01
Many studies have shown that there are good reasons to claim very low predictability of currency returns; nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J. James, Simple trend-following strategies in currency trading, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the technical analysis. In this article we introduced various statistical quantities examining role of single temporal discretized trend or multitude of grouped trends corresponding to different time delays. Our specific analysis based predominantly on Euro-dollar currency pair data at the one minute frequency suggests the importance of cumulative nonrandom effect of trends on the potential forecasting performance.
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NASA Astrophysics Data System (ADS)
Korotkov, E. V.; Korotkova, M. A.
2017-01-01
The purpose of this study was to detect latent periodicity in the presence of deletions or insertions in the analyzed data, when the points of deletions or insertions are unknown. A mathematical method was developed to search for periodicity in the numerical series, using dynamic programming and random matrices. The developed method was applied to search for periodicity in the Euro/Dollar (Eu/) exchange rate, since 2001. The presence of periodicity within the period length equal to 24 h in the analyzed financial series was shown. Periodicity can be detected only with insertions and deletions. The results of this study show that periodicity phase shifts, depend on the observation time. The reasons for the existence of the periodicity in the financial ranks are discussed.
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Estimating time-varying conditional correlations between stock and foreign exchange markets
NASA Astrophysics Data System (ADS)
Tastan, Hüseyin
2006-02-01
This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.
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Exponentially damped Lévy flights, multiscaling, and exchange rates
NASA Astrophysics Data System (ADS)
Matsushita, Raul; Gleria, Iram; Figueiredo, Annibal; Rathie, Pushpa; Da Silva, Sergio
2004-02-01
We employ our previously suggested exponentially damped Lévy flight (Physica A 326 (2003) 544) to study the multiscaling properties of 30 daily exchange rates against the US dollar together with a fictitious euro-dollar rate (Physica A 286 (2000) 353). Though multiscaling is not theoretically seen in either stable Lévy processes or abruptly truncated Lévy flights, it is even characteristic of smoothly truncated Lévy flights (Phys. Lett. A 266 (2000) 282; Eur. Phys. J. B 4 (1998) 143). We have already defined a class of “quasi-stable” processes in connection with the finding that single scaling is pervasive among the dollar price of foreign currencies (Physica A 323 (2003) 601). Here we show that the same goes as far as multiscaling is concerned. Our novel findings incidentally reinforce the case for real-world relevance of the Lévy flights for modeling financial prices.
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Correlation filtering in financial time series (Invited Paper)
NASA Astrophysics Data System (ADS)
Aste, T.; Di Matteo, Tiziana; Tumminello, M.; Mantegna, R. N.
2005-05-01
We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al.,1 we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).
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GSM-Railway as part of the European Rail Traffic Management System
NASA Astrophysics Data System (ADS)
Bibac, Ionut
2007-05-01
GSM-R is a vital component inside the ERTMS which is also an essential element of European Community rail projects; investment in equipping and the rolling stock with ERTMS could reach 5 billion eurodollars in the period 2007-2016. GSM-R is the result of over ten years of collaboration between the various European railway companies, the railway communication industry and the different standardization bodies. GSM-R provides a secure platform for voice and data communication between the operational staff of the railway companies including drivers, dispatchers, shunting team members, train engineers, and station controllers. It delivers advanced features such as group calls, voice broadcast, location based connections, and call pre-emption in case of an emergency, which significantly improves communication, collaboration, and security management across operational staff members. Taking into account the above mentioned, the paper will permit to audience to discover the GSM-R network architecture, services and applications proposed by this technology together with the future development and market situation due to the market liberalization.
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Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting
Ghazali, Rozaida; Herawan, Tutut
2016-01-01
Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network. PMID:27959927
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Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.
Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut
2016-01-01
Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.