scholarly journals Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Peng Zheng ◽  
Bin Liu ◽  
Zhongli Zhou
Keyword(s):  
Time Series ◽  
Foreign Exchange ◽  
Financial Market ◽  
Multiresolution Analysis ◽  
Foreign Exchange Market ◽  
Financial Time Series ◽  
Time Windows ◽  
Memory Model ◽  
Forecasting Returns ◽  
Theory Research

Memory in finance is the foundation of a well-established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving V-statistic on the basis of a modified R/S method to determine whether the time series has a long-range dependence and subsequently to apply wavelet-based multiresolution analysis to study the multifractality of the financial time series to determine the initial data windows. Finally, we check the moving V-statistic estimation in wavelet analysis in the same condition; the paper selects the volatilities of the gold foreign exchange rates to evaluate the moving V-statistic. According to the results, the method of testing memory established in this paper can identify the breakpoint of the memories effectively. Furthermore, this method can provide support for forecasting returns in the financial market.

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

Volatility transmission in African foreign exchange markets

2016 ◽  
Vol 7 (2) ◽  
pp. 205-224 ◽  
Author(s):  
Emmanuel Carsamer
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Financial Market ◽  
Foreign Exchange Market ◽  
Exchange Rate Volatility ◽  
Exchange Rate Policy ◽  
Future Research ◽  
Research Approach ◽  
Content Type ◽  
Volatility Transmission

Purpose – The concept of volatility transmission and co-movement has witnessed a resurgence in the international finance literature in recent years after the black swan events which gave evidence of financial market linkages. The purpose of this paper is to examine the dynamic sources of volatility transmission in the foreign exchange market in recent financial market integration in Africa. Design/methodology/approach – A conceptual framework was adapted from the extant literature and was used as the basis of modeling exchange rate volatility transmission. This paper adopts a quantitative research approach and opts for augmented DCC model to empirically unearth the sources of exchange rate volatility transmission. Findings – The key findings of the study are that, the African market is more prone to shock from outside than in the region. Macroeconomic news surprises influence volatility transmission and co-movements. Robust support is found for trade balance, interest rate and gross domestic product. These findings clearly demonstrate the low level of financial development and challenges that sometimes exist in exchange rate-policy implementation by policy makers. Research limitations/implications – Interested academics and practitioners working in the area might incorporate bilateral investment into the model of exchange rate correlation in future research. Originality/value – Unilaterally considering exchange rate volatility transmission and subsequent augmentation of the DCC model, this study makes a modest contribution to the examination of exchange rate correlations in Africa. This study makes an important contribution in not only addressing this imbalance, but more importantly improving the relative literature on exchange rate volatility transmission.

scholarly journals Modeling the segment interactions of Ukraine’s financial market

2020 ◽  
Vol 17 (2) ◽  
pp. 101-112 ◽  
Author(s):  
Liudmyla Prymostka ◽  
Іryna Krasnova ◽  
Ganna Kulish ◽  
Andrii Nikitin ◽  
Valentyna Shevaldina
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Foreign Exchange Market ◽  
Credit Market ◽  
Market Segments ◽  
State Budget ◽  
Dynamic Relationship ◽  
Registration Number ◽  
External Conditions ◽  
State Registration

This study is devoted to assessing the level of individual segments interconnectedness within the financial market of Ukraine (FMU) and their dynamics in uncertain conditions. The methodology of the systematic approach is used to investigate the dynamic relationship between individual segments of the financial market of Ukraine, namely credit (deposit-credit) market, stock market (market of securities), government securities market, currency market, and interbank market. The study of financial market dynamics focuses on the description of the price indicators of individual market segments, which are monitored using time series analysis and statistical methods. The results of the time series assessment revealed the fractal characteristics of the Ukrainian financial market as a measure of sustainability (namely inertia). It is revealed that all segments of the financial market, except credit, are characterized by persistence. It is established that the development of market segments is uneven and is characterized as bifurcation. The credit segment is addicted to insider behavior and has the highest risk concentration. It is revealed that the foreign exchange market is still in crisis. The results of modeling the correlation relationships between market segments have shown that, in the presence of such relationships, they differ in the strength and nature of the interaction. They are volatile, unstable, and situational, dependent on external conditions. The credit market has a relationship with other segments, not significantly strong but stable. The results of the analysis indicate the dynamic development of segments within the Ukrainian financial market in the presence of interconnections between them. AcknowledgmentThe study was conducted within the framework of the State Budget of the Kyiv National Economic University named after Vadym Hetman on the topic “Innovative development of banking activities in the integrated financial environment” (state registration number 0117U001178).  

scholarly journals Central bank's communication and markets' reactions: Polish evidence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janusz Brzeszczyński ◽  
Jerzy Gajdka ◽  
Tomasz Schabek ◽  
Ali M Kutan
Keyword(s):  
Monetary Policy ◽  
Foreign Exchange ◽  
Financial Market ◽  
Foreign Exchange Market ◽  
Emerging Market ◽  
Public Information ◽  
Exchange Market ◽  
Market Segments ◽  
Content Type ◽  
The Impact

PurposeThis study contributes to the pool of knowledge about the impact of monetary policy communication of central banks on financial instruments' prices and assets' value in emerging markets.Design/methodology/approachEmpirical analysis is executed using the National Bank of Poland (NBP) announcements about its monetary policy covering the data from the broad financial market in its three main segments: stock market, foreign exchange market and bonds market. The reactions are measured relative to the changes in the NBP announcements and also with respect to investors' expectations. Autoregressive conditional heteroscedasticity (ARCH) models with dummy variables are used as the main methodological tool.FindingsBonds market and foreign exchange market are the most sensitive market segments, while interest rate and money supply are the most influential types of announcements. The changes of the revealed new macroeconomic figures had more impact on assets' prices movements than the deviations from their expectations. Moreover, greater diversity of the Monetary Policy Council (MPC) members' opinions on the voted motions, captured in the MPC voting reports, is associated with more cases of statistically significant NBP communication events.Practical implicationsThe findings have direct relevance for fund managers, portfolio analysts, investors and also for financial market regulators.Originality/valueThe results provide novel evidence about how the emerging financial market responds to monetary policy announcements. They help understand the nature of the impact of public information on financial assets' valuation and on movements of their prices, analysed comprehensively in three market segments, in the emerging market environment.

Fractal Dimensional Analysis in Financial Time Series

GIS Business ◽  
2016 ◽  
Vol 10 (6) ◽  
pp. 46-52
Author(s):  
S. J. Bhatt ◽  
H. V. Dedania ◽  
Vipul R. Shah
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Long Range ◽  
Financial Market ◽  
Financial Time Series ◽  
Local Market ◽  
Jones Index ◽  
Financial Time ◽  
Predictability Index

A predictability index for time series of a financial market vector consisting of chosen market parameters is suggested providing a measure of long range predictability of the market. It is based on fractional Brownian motion that includes Brownian motion as a particular case followed by the time series of financial market parameters. By analyzing respective time series, these indices are computed for parameters like volatility, FII investments in the local market, IIP numbers, CPI numbers, Dow Jones Index, different stock market indices, currency rates, and gold prices.

Robert Engle and Clive Granger: New Spheres of Economic Research (Nobel Prize in Economics 2003)

2004 ◽  
pp. 37-48
Author(s):  
G. Kantorovich ◽  
M. Touruntseva
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Nobel Prize ◽  
Financial Time Series ◽  
Market Analysis ◽  
Time Series Model ◽  
Economic Research ◽  
Estimation Methods ◽  
Financial Time ◽  
Financial Market Analysis

This paper is dedicated to the achievements of Robert Engle and Clive Granger which allowed to overcome a serious crisis in macroeconomics and financial market analysis. The main concepts of cointegration theory and different estimation methods of cointegration equations are considered in the first part of the paper. The areas of application of cointegration theory and possible extensions are briefly described as well. The financial time series model with conditional heteroskedastisity is analyzed in the second part of the paper. The main prerequisites of the method suggested by R. Engle are formulated and its extensions and areas of application are defined.

scholarly journals Scenario Analysis of the Russian Forex Market Development Strategies

2018 ◽  
Vol 11 (6) ◽  
pp. 26-38
Author(s):  
E. A. Zvonova
Keyword(s):  
Foreign Exchange ◽  
Financial Market ◽  
National Economy ◽  
Foreign Exchange Market ◽  
International Monetary System ◽  
Development Strategies ◽  
Market Development ◽  
Research Information ◽  
Macroeconomic Variables ◽  
Scenario Model

The subject of the research is the foresight into development strategies of the foreign exchange sector of the Russian financial market under the new geo-economic conditions and in the context of the international monetary system reform and sanctions on Russian transactions in the international financial market. The relevance of the problem is caused by the ruble volatility and strong fluctuations in the values of macroeconomic variables after 2014. The purpose of the research was to develop a scenario model for the development of the Russian forex market for the next three years based on three scenario forecasts: optimistic, pessimistic and conservative. A scenario model of the Russian foreign exchange market development was built by superimposing scenarios of the national economy development on the forex market development scenarios along with assessment of the possible impact of the forex market development on other national financial market segments. The model is based on international macroeconomic variables statistics, the state of Russia’s payment balance and also on a comparative analysis of the development indices of national economies of raw materials exporting countries and the national economy of Russia. For data aggregation and reduction to a single format, an information-logical model for formation of the research information base was developed. The obtained scenario model for the development of the Russian financial market forex sector has a high predictive capability. The paper concludes that, based on the scenario model, the forex sector of the Russian financial market will be fairly stable over the next three years, which should be taken into account by the Bank of Russia when making decisions on the forex policy and creation of international currency reserves.

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