SPILLOVER EFFECT OF THE FEDERAL RESERVE’S FORWARD GUIDANCE ON CHINA’S FINANCIAL MARKETS: FROM MECHANISM ANALYSIS TO EMPIRICAL TEST

2021 ◽  
pp. 1-32
Author(s):  
XIN DENG ◽  
LIN GE ◽  
XUAN WU
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Empirical Test ◽  
Spillover Effect ◽  
Mechanism Analysis ◽  
Money Markets ◽  
Forward Guidance ◽  
Egarch Model ◽  
Before And After

This study analyzed the channels responsible for the spillover effect of the US Federal Reserve’s (Fed’s) forward guidance on China’s financial markets with an event study and EGARCH model with data collected over the past decade. The Fed’s forward guidance affects China’s foreign exchange, bond, stock and money markets. In the three trading days before and after the event, China’s foreign exchange market had an instantaneous reaction, the bond and stock market had lagged reactions, and the money market reaction lasted for the full event window. The Fed’s forward guidance on China’s financial market differs based on the Fed’s monetary policy, guidance type and whether the guidance content is adjusted or not.

Global markets as global conversations

2007 ◽  
Vol 27 (5-6) ◽  
Author(s):  
Karin Knorr Cetina
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Global Market ◽  
Global Markets ◽  
Economic Aspects ◽  
Exchange Market ◽  
Conversational Interaction ◽  
Interaction System ◽  
Streaming Flow

AbstractFinancial markets are one of the most iconic and influential structures of our time. The foreign exchange market in particular is also the most genuinely global market—and the largest market worldwide, with an average daily turnover of 1.8 trillion US dollars. The foreign exchange market is also structurally like a massive conversational interaction system; many of its transactions are conducted through electronically mediated ‘conversations’. Transactions not conducted through conversations but through an electronic broker also display a sequential turn-taking structure. In this paper, I analyze the streaming ‘flow’ architecture of this market in terms of its sequential structures and their technological and economic aspects. I also specify and analyze several types of texted sequences that articulate and illustrate the response-based interaction system of this market. I argue that informational sequences are particularly important; the informational liquidity of this market sustains and supports the market's economic liquidity.

‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects

2019 ◽  
Vol 18 (2_suppl) ◽  
pp. S183-S212 ◽  
Author(s):  
Suparna Nandy (Pal) ◽  
Arup Kr. Chattopadhyay
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Volatility Spillover ◽  
Exchange Market ◽  
Causality Test ◽  
Asymmetric Volatility ◽  
Indian Stock Market ◽  
Domestic Stock

The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multi­variate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17

scholarly journals COMPUTATION INTELLIGENCE BASED DAILY ALGORITHMIC STRATEGIES FOR TRADING IN THE FOREIGN EXCHANGE MARKET

2018 ◽  
Author(s):  
Nijolė Maknickienė ◽  
Ieva Kekytė ◽  
Algirdas Maknickas
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Processing Unit ◽  
Exchange Market ◽  
Data Prediction ◽  
Open Source Framework ◽  
Market State ◽  
Port Systems ◽  
Graphical Processing

Successful trading in financial markets is not possible without a support system that manages the preparation of the data, prediction system, and risk management and evaluates the trading efficien-cy. Selected orthogonal data was used to predict exchange rates by applying recurrent neural network (RNN) software based on the open source framework Keras and the graphical processing unit (GPU) NVIDIA GTX1070 to accelerate RNN learning. The newly developed software on the GPU predicted ten high-low distributions in approximately 90 minutes. This paper compares different daily algorith-mic trading strategies based on four methods of portfolio creation: split equally, optimisation, orthogonality, and maximal expectations. Each investigated portfolio has opportunities and limita-tions dependent on market state and behaviour of investors, and the efficiencies of the trading sup-port systems for investors in foreign exchange market were tested in a demo FOREX market in real time and compared with similar results obtained for risk-free rates.

scholarly journals Spatial-Temporal Analysis the Dynamics of Changes on the Foreign Exchange Market: an Empirical Estimates from Ukraine

2020 ◽  
Vol 1 (2) ◽  
pp. 1-6
Author(s):  
Oleg Vasiurenko ◽  
Vyacheslav Lyashenko ◽  
Valeria Baranova ◽  
Zhanna Deineko
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Emerging Economies ◽  
Foreign Exchange Market ◽  
Temporal Analysis ◽  
Wavelet Coherence ◽  
Exchange Market ◽  
Empirical Estimates ◽  
Market Trends ◽  
Spatial Temporal Analysis

The foreign exchange market plays an important role in the formation and development of financial markets. This market is of particular importance for emerging economies. To understand market trends (to understand and develop a strategy for its development), it is necessary to analyze historical data. It is also important to use different methods to carry out this analysis. Based on this, the paper analyzes the foreign exchange market in Ukraine for the period 2014-2018. For this analysis, the wavelet coherence methodology is used. This made it possible to assess the development of the foreign exchange market in Ukraine.

scholarly journals An Empirical Test on Linkage Between Foreign Exchange Market and Stock Market: Evidence from Hungary, Czech Republic, Poland and Romania

2017 ◽  
Vol 13 (31) ◽  
pp. 25
Author(s):  
Ngo Thai Hung
Keyword(s):  
Exchange Rate ◽  
Czech Republic ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Stock Return ◽  
Foreign Exchange Market ◽  
Empirical Test ◽  
Causality Test ◽  
Vector Auto Regression ◽  
Auto Regression

The paper aims to examine the causal relationship between the stock prices and exchange rates in Hungary, Czech Republic, Poland and Romania. The investigation employs Granger’s Causality test and Vector Auto Regression technique on monthly stock return and the foreign exchange rate for the period October 31, 2008 to September 18, 2017. The major findings of the study that there is no Granger’s causality between the exchange rate return and stock return in these countries. The study also uses Vector Auto Regression modeling to confirm that though stock return and exchange rate are related to each other but any consistent relationship does not exist between them. Our results have provided beneficial information for investors, government policies and researchers.

A Study on Unobserved Structural Innovations of Oil Price: Evidence from Global Stock, Bond, Foreign Exchange, and Energy Markets

2015 ◽  
Vol 18 (01) ◽  
pp. 1550004 ◽  
Author(s):  
Jungho Baek ◽  
Ji-Yong Seo
Keyword(s):  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Spillover Effect ◽  
Oil Prices ◽  
Energy Market ◽  
Oil Shocks ◽  
Exchange Market ◽  
Demand Shock ◽  
Supply Shock ◽  
Global Stock

This study examines the effects of oil shocks by their respective causes and of volatility spillover including leverage effects. Previous studies did not analyze oil factor by categorizing it into three components (supply shock, demand shock, and market shock) as determinants of rate of return in stock markets, a key issue in finance. Results show that oil shocks determine returns in the global stock market, bond market, foreign exchange market, and energy market, and that their effects vary by types of markets, levels of oil prices, and types of oil shocks. Second, the leverage effect of oil shocks and the spillover effect of volatility in demand shock and market shock are mostly statistically significant during periods characterized by high oil prices.

scholarly journals Competition of the world leading currencies: Features of the current stage

2021 ◽  
Vol 20 (11) ◽  
pp. 2074-2088
Author(s):  
Vladimir K. BURLACHKOV
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Global Economy ◽  
Foreign Exchange Market ◽  
Global Market ◽  
Leading Role ◽  
The World ◽  
Current Stage ◽  
The U.S

Subject. The article analyzes the competition of the world leading currencies in the global economy, specifics of the current stage, trends in the role of particular currencies in the global market. Objectives. The purpose is to review the current positions of the U.S. Dollar, Euro and Yuan in the global financial markets, assess prospects for maintaining the leading role of the U.S. Dollar, development trends in the position of Euro and Yuan. Methods. I applied the content analysis of available sources, provide a historical overview of issues under consideration, scrutinized the estimates of financial analysts. Results. The paper unveils reasons for increased competition of the leading currencies (U.S. Dollar, Euro, Yuan) in the global foreign exchange market, which include an increase in the scale of payment transactions in the global financial and commodity markets. It also reveals trends in the use of particular currencies in foreign trade and financial transactions, evaluates prospects for the use of specific world currencies in the global economy. Conclusions. At present, U.S. Dollar maintains its leading positions. However, in the future, an increase in the use of Euro- and Yuan-denominated transactions should be expected in the commodity and financial markets due to enlarged presence of Chinese companies in the global economy. Further development of European integration can ensure the expansion of the single European currency in the global financial market. The share of Yuan in foreign exchange reserves of central banks tends to increase. Private investors' demand for Yuan is also expected to grow.

scholarly journals Applications of Financial Technology in Foreign Exchange Market

2021 ◽  
Vol 233 ◽  
pp. 01160
Author(s):  
Wei Li
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Service Industry ◽  
Advanced Technology ◽  
Exchange Market ◽  
Financial Industry ◽  
Block Chain ◽  
Exchange Management ◽  
Financial Technology

Financial technology (Fintech), including a series of advanced technologies such as big data, artificial intelligence and block-chain, has been gradually applied to various industries after years of development and will become the major driver of the future financial industry. As one of the largest financial markets in the world, the traditional foreign exchange service industry is gradually entering the era of fintech, bringing new vitality to the foreign exchange market through advanced technology and improving the efficiency of foreign exchange management. However, while enjoying the opportunities brought by fintech to the foreign exchange field, practitioners in both fintech and the foreign exchange industry should also actively face the challenges and try to build a safe and efficient foreign exchange market environment.

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