Showing papers on "Foreign exchange market published in 2018"
TL;DR: This article investigated the internal and external categorical economic policy uncertainty (EPU) spillovers between the US and Japan using a novel extension of the TVP-VAR connectedness approach.
178 citations
TL;DR: The authors analyzes the role of real exchange rate (RER) policies in promoting economic development and shows that a stable and competitive RER policy may correct for this externality and other related market failures.
110 citations
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TL;DR: In this paper, the authors developed a model to predict the impact of oil prices on stock market indices for Russia, Brazil, and the United States based on the FIGARCH model of the long memory.
Abstract: This paper proposes the volatility spillover effect between stock and foreign exchange markets in both directions in oil exporting countries – Russia and Brazil. The data sample consists of daily observations. The method is based on FIGARCH model of the long memory. For emerging markets, volatility spillover is observed mainly in one direction: from the currency market to stock market. Calculations show that long memory is present in the dynamics of volatility, when models take into account structural breaks and frictions. We develop a model to predict the impact of oil prices on stock market indices for Russia, Brazil. The volatility spillover effect is observed in one direction: from the exchange rate to stock market. Calculations show that long memory is present in the dynamics of volatility, when models take into account structural breaks and frictions. This paper focuses on new method for forecasting of volatility (taking into account the structural breaks) on the base of FIGARCH model. The financial markets became more integrated after the World Economic Crisis of 2008-2009. The paper shows that volatility can be predicted using the FIGARCH model if the structural breaks are incorporated in the model. The paper should be of interest to readers in the areas of economic forecasting on the base of long memory models.
72 citations
TL;DR: A new system for short-term speculation in the foreign exchange market, based on recent reinforcement learning (RL) developments, which includes new state and reward signals, and a method for more efficient use of available historical tick data that provides improved training quality and testing accuracy.
67 citations
TL;DR: In this article, the fluctuation properties of the rapidly emerging Bitcoin market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects.
Abstract: Based on 1-minute price changes recorded since year 2012, the fluctuation properties of the rapidly-emerging Bitcoin (BTC) market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects. The findings are compared to the stylized facts of mature world markets. While early trading was affected by system-specific irregularities, it is found that over the months preceding Apr 2018 all these statistical indicators approach the features hallmarking maturity. This can be taken as an indication that the Bitcoin market, and possibly other cryptocurrencies, carry concrete potential of imminently becoming a regular market, alternative to the foreign exchange (Forex). Since high-frequency price data are available since the beginning of trading, the Bitcoin offers a unique window into the statistical characteristics of a market maturation trajectory.
65 citations
TL;DR: In this paper, the authors studied the relationship between giving financial advice and the disposition effect in an online trading environment and found that leader traders are more susceptible to the effect than investors who are not being followed by any other trader.
Abstract: This article studies the relationship between giving financial advice and the disposition effect in an online trading environment. Our empirical findings suggest that leader traders are more susceptible to the disposition effect than investors who are not being followed by any other trader. Using a difference-in-differences approach, we show that becoming a first-time financial advisor increases the disposition effect. This finding holds for investors who engage in foreign exchange trading and for investors who trade stocks and stock market indices. The increased behavioral bias may be explained by leaders feeling responsible to their followers, by a fear of losing followers when admitting a poor investment decision, or by an attempt by newly appointed leaders to manage their social image and self-image.
43 citations
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TL;DR: In this article, the authors proposed an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent, which enables them to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used epsilon-greedy exploration.
Abstract: An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used epsilon-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
34 citations
TL;DR: In this article, the performance of a simple, countercyclical reserve requirement rule is studied in a dynamic stochastic model of a small open economy with financial frictions, imperfect capital mobility, a managed float regime, and sterilized foreign exchange market intervention.
33 citations
TL;DR: In this paper, a multivariate GARCH model was employed to model the transmission mechanism of mean return, return spillover and shock spillover between the stock market and the foreign exchange market, using their return series.
31 citations
TL;DR: In this article, a dynamic general equilibrium model of intermediation in the FOREX market is proposed, where immediate trade between ultimate buyers and sellers of foreign currencies is obstructed by search frictions due to geographic dispersion.
Abstract: The FOREX market is an over-the-counter market (in fact, the largest in the world) characterized by bilateral trade, intermediation, and significant bid-ask spreads. The existing international macroeconomics literature has failed to account for these stylized facts largely due to the fact that it models the FOREX as a standard Walrasian market, therefore overlooking some important institutional details of this market. In this paper, we build on recent developments in monetary theory and finance to construct a dynamic general equilibrium model of intermediation in the FOREX market. A key concept in our approach is that immediate trade between ultimate buyers and sellers of foreign currencies is obstructed by search frictions (e.g., due to geographic dispersion). We use our framework to compute standard measures of FOREX market liquidity, such as bid-ask spreads and trade volume, and to study how these measures are affected both by macroeconomic fundamentals and the FOREX market microstructure. We also show that the FOREX market microstructure critically affects the volume of international trade and, consequently, welfare. Hence, our paper highlights that modeling the FOREX as a frictionless Walrasian market is not without loss of generality.
28 citations
TL;DR: This article showed that political risk is priced in the cross-section of currency momentum and contains information beyond other risk factors, and that risk compensation is mainly justified by the different exposures of foreign currencies in the momentum portfolio to U.S. political shocks.
Abstract: Using a measure of political risk, relative to the United States, that captures unexpected political conditions, we show that political risk is priced in the cross section of currency momentum and contains information beyond other risk factors. Our results are robust after controlling for transaction costs, reversals, and alternative limits to arbitrage. The global political environment affects the profitability of the momentum strategy in the foreign exchange market; investors following such strategies are compensated for the exposure to the global political risk of those currencies they hold, that is, the past winners, and exploit the lower returns of loser portfolios. The risk compensation is mainly justified by the different exposures of foreign currencies in the momentum portfolio to U.S. political shocks, which is the main component of global political risk.
01 Jan 2018
TL;DR: In this article, the degree of passthrough of the official and parallel exchange rates to inflation as well as the relationship between exchange rate volatility and inflation in Nigeria based on monthly time series data (January 2006 to December 2015).
Abstract: In recent times, the Nigerian economy has been experiencing significant exchange rate fluctuations, particularly depreciation in the foreign exchange market which has been accompanied with inflation. Thus, this paper investigates the degree of passthrough of the official and parallel exchange rates to inflation as well as the relationship between exchange rate volatility and inflation in Nigeria based on monthly time series data (January 2006 to December 2015). In achieving its objectives, the study employs the Generalised Auto Regressive Conditional Heteroscedasticity (GARCH), technique, which was complemented using Co-integration, Vector Error Correction Model, Variance Decomposition and Impulse Response techniques. The results suggest that the parallel exchange rate passes through to inflation in the short run while the officialexchange rate passes through to inflation in the long-run exclusively. It also reveals that exchange rate volatility has a positive and significant effect on inflation in the long-run
TL;DR: In this article, the authors study the correlations of exchange rate volatility in the global foreign exchange market based on complex network graphs and introduce a measure of the impact of individual currency based on its partial correlations with other currencies.
Abstract: We study the correlations of exchange rate volatility in the global foreign exchange(FX) market based on complex network graphs. Correlation matrices (CM) and the theoretical information flow method (Infomap) are employed to analyze the modular structure of the global foreign exchange network. The analysis demonstrates that there exist currency modules in the network, which is consistent with the geographical nature of currencies. The European and the East Asian currency modules in the FX network are most significant. We introduce a measure of the impact of individual currency based on its partial correlations with other currencies. We further incorporate an impact elimination method to filter out the impact of core nodes and construct subnetworks after the removal of these core nodes. The result reveals that (i) the US Dollar has prominent global influence on the FX market while the Euro has great impact on European currencies; (ii) the East Asian currency module is more strongly correlated than the European currency module. The strong correlation is a result of the strong co-movement of currencies in the region. The co-movement of currencies is further used to study the formation of international monetary bloc and the result is in good agreement with the consideration based on international trade.
TL;DR: In this paper, the authors introduce a method to infer lead-lag networks of agents' actions in complex systems, which open the way to both microscopic and macroscopic states prediction in such systems.
Abstract: We introduce a method to infer lead-lag networks of agents’ actions in complex systems. These networks open the way to both microscopic and macroscopic states prediction in such systems. We apply t...
TL;DR: In this article, the authors investigate the dynamics of efficiency and long memory, and the impact of trading volume on the efficiency of returns and volatilities of four major traded currencies, namely, the EUR, GBP, CHF and JPY.
Abstract: We investigate the dynamics of efficiency and long memory, and the impact of trading volume on the efficiency of returns and volatilities of four major traded currencies, namely, the EUR, GBP, CHF and JPY. We do so by implementing full sample and rolling window multifractal detrended fluctuation analysis (MF-DFA) and a quantile-on-quantile (QQ) approach. This paper sheds new light by employing high frequency (5-min interval) data spanning from Jan 1, 2007 to Dec 31, 2016. Realized volatilities are estimated using Andersen et al.’s (2001) measure, while the QQ method employed is drawn from Sim and Zhou (2015). We find evidence of higher efficiency levels in the JPY and CHF currency markets. The impact of trading volume on efficiency is only significant for the JPY and CHF currencies. The GBP currency appears to be the least efficient, followed by the EUR. Implications of the results are discussed.
TL;DR: This paper applied long-memory techniques (both parametric and semi-parametric) to examine whether Brexit has led to any significant changes in the degree of persistence of the FTSE (Financial Times Stock Index) 100 Implied Volatility Index (IVI) and of the British pound's implied volatilities (IVs) vis-a-vis the main currencies traded in the FOREX (foreign exchange market), namely the euro, the US dollar and the Japanese yen.
Abstract: This paper applies long-memory techniques (both parametric and semi-parametric) to examine whether Brexit has led to any significant changes in the degree of persistence of the FTSE (Financial Times Stock Index) 100 Implied Volatility Index (IVI) and of the British pound’s implied volatilities (IVs) vis-a-vis the main currencies traded in the FOREX (foreign exchange market), namely the euro, the US dollar and the Japanese yen We split the sample to compare the stochastic properties of the series under investigation before and after the Brexit referendum, and find an increase in the degree of persistence in all cases except for the British pound-yen IV, whose persistence has declined after Brexit These findings highlight the importance of completing swiftly the negotiations with the European Union (EU) to achieve an appropriate Brexit deal
TL;DR: In this paper, the authors examined short-term price reactions after one-day abnormal price changes and whether they create exploitable profit opportunities in various financial markets and found that a strategy based on counter-movements after overreactions does not generate profits in the FOREX and the commodity markets, but it is profitable in the US stock market.
Abstract: This paper examines short-term price reactions after one-day abnormal price changes and whether they create exploitable profit opportunities in various financial markets. A t-test confirms the presence of overreactions and also suggests that there is an "inertia anomaly", i.e. after an overreaction day prices tend to move in the same direction for some time. A trading robot approach is then used to test two trading strategies aimed at exploiting the detected anomalies to make abnormal profits. The results suggest that a strategy based on counter-movements after overreactions does not generate profits in the FOREX and the commodity markets, but it is profitable in the case of the US stock market. By contrast, a strategy exploiting the "inertia anomaly" produces profits in the case of the FOREX and the commodity markets, but not in the case of the US stock market.
23 May 2018
TL;DR: A novel method is proposed to detect regime change, which makes use of a data-driven approach, that of directional change (DC), which demonstrates that the DC approach is as effective as the time-series approach in detecting regime changes.
Abstract: A regime change is a significant change in the collective trading behaviour in a financial market. Being able to detect the occurrence of regime change could lead to a better understanding and monitoring of financial markets. In this paper, a novel method is proposed to detect regime change, which makes use of a data-driven approach, that of directional change (DC). Compared to the conventional approach of using time series analysis, DC is an alternative approach to sample price movement. As variables observed under time series do not apply to DC, our first contributions is the identification of a new relevant indicator for regime change detection. Our second contribution is the comparison of both the DC approach and time series analysis, their ability to achieve regime change detection. The ability of both approaches in regime change detection is examined over a period of market uncertainty, that of Brexit. The results demonstrate that the DC approach is as effective as the time-series approach in detecting regime changes. Moreover, the DC approach is encouraging because some market regime changes are detected under DC, but are not found under time series. That means they support each other in the detection of regime change, and can also provide extra information to complement each other. Together, regime changes detected under both DC and time series provide a better insight into the market, which market participants and regulators could benefit from.
TL;DR: The previous work on ICRRP is extended by incorporating a multi-dimensional jump diffusion process to model the state dynamics, and hence, enhance the viability of the extant model for applications and employs a novel minimum cost operator that simplifies the computations of the optimal solutions.
Abstract: Impulse control with random reaction periods (ICRRP) is used to derive a country’s optimal foreign exchange (forex) rate intervention policy when the forex market reacts to the interventions. This paper extends the previous work on ICRRP by incorporating a multi-dimensional jump diffusion process to model the state dynamics, and hence, enhance the viability of the extant model for applications. Furthermore, we employ a novel minimum cost operator that simplifies the computations of the optimal solutions. Finally, we demonstrate the efficacy of our framework by finding a market-reaction-adjusted optimal central bank intervention (CBI) policy for a country. Our numerical results suggests that market reactions and the jumps in the forex market are complements when the reactions increase the forex rate volatility; otherwise, they are substitutes.
TL;DR: Karolyi et al. as discussed by the authors show that a government pursuing a nonpublic, partially informative price target in a model of strategic market-order trading and segmented dealership generates equilibrium price differentials among fundamentally identical assets by clouding dealers' inference about the targeted asset's payoff from its order flow, to an extent complexly dependent on existing price formation.
Abstract: Direct government intervention in a market may induce violations of the law of one price in other, arbitrage-related markets. I show that a government pursuing a nonpublic, partially informative price target in a model of strategic market-order trading and segmented dealership generates equilibrium price differentials among fundamentally identical assets by clouding dealers’ inference about the targeted asset’s payoff from its order flow, to an extent complexly dependent on existing price formation. I find supportive evidence using a sample of American Depositary Receipts and other cross-listings traded in the major U.S. exchanges, along with currency interventions by developed and emerging countries between 1980 and 2009. Received May 18, 2016; editorial decision May 10, 2017 by Editor Andrew Karolyi.
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TL;DR: An Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm and develops an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent.
Abstract: An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used epsilon-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
TL;DR: The authors conducted an extensive mixed-method study of exchange rate determination in the Brazilian foreign exchange market and found that currency internationalisation has been mediated through a structured and hierarchic international monetary system which fundamentally distinguishes exchange rate drivers in emerging economies from those in developed ones.
Abstract: This paper conducts an extensive mixed-method study of exchange rate determination in the Brazilian foreign exchange market. It combines semi-structured interviews with foreign exchange market participants in Brazil and London and advanced time-series econometrics. In line with Post Keynesian theory and critical realist ontology, the interviews uncover the context-specific expectations and underlying processes and structures that condition exchange rate dynamics in Brazil and emerging economies more generally. The results point to important structural changes in Brazil’s financial integration in the form of currency internationalisation and financialisation. Moreover, they show that this internationalisation has been mediated through a structured and hierarchic international monetary system which fundamentally distinguishes exchange rate drivers in emerging economies from those in developed ones.
TL;DR: Numerical examples demonstrated that the proposed approach trained with historical data is able to produce optimizing results on forecasting the future foreign exchange rates for a very long period, and also show the potential of the proposed Approach in real applications.
TL;DR: The authors identify intraday jumps and cojumps in exchange rates controlling for volatility patterns and relate these events to pre-scheduled macroeconomic news and market conditions, showing that jump events are consistent with rational dealer quoting behavior.
Abstract: I identify intraday jumps and cojumps in exchange rates controlling for volatility patterns and relate these events to pre-scheduled macroeconomic news and market conditions. Event study results show that preceding jump and cojump events, exchange rate quote volume, illiquidity, signed order flow, and informed trades are at heightened levels revealing that jump events are consistent with rational dealer quoting behavior. Following jump and cojump events, quote volume and return variance remain at heightened levels while illiquidity, informed trade, and signed order flow remain at depressed levels providing evidence that order flow following jump events is largely uninformed liquidity provision.
TL;DR: In this paper, the impact of macroeconomic news announcements and the communication of the monetary policy settings of the ECB and the Fed on the forex markets of new EU members was analyzed.
TL;DR: This paper introduces a new algorithm, inspired by the behaviour of macromolecules in dissolution, to model the evolution of the FOREX market, called the ENMX (elastic network model for FORex market) algorithm, which allows the system to escape from a potential local minimum, so it can reproduce the unstable nature of theFOREX market.
TL;DR: The authors examined the failure of failure to predict the financial crisis in non-securitized markets, focusing on the complexity of the financial system and less useful for understanding crises in nonsecure markets.
Abstract: Existing accounts of failure to predict the financial crisis focus on the complexity of the financial system, and are less useful for understanding crises in non-securitized markets. We examine the...
TL;DR: This article found that traders increase trade sizes, trade size variability, and number of trades with gains, and less with losses, following winning weeks, relative to losing weeks, consistent with more intense learning in early trading periods.
Abstract: We document evidence consistent with retail day traders in the Forex market attributing random success to their own skill and, as a consequence, increasing risk taking. Although past performance does not predict future success for these traders, traders increase trade sizes, trade size variability, and number of trades with gains, and less with losses. There is a large discontinuity in all of these trading variables around zero past week returns: e.g., traders increase their trade size dramatically following winning weeks, relative to losing weeks. The effects are stronger for novice traders, consistent with more intense “learning” in early trading periods.
TL;DR: The authors analyze relevant public information provided by new media to predict the movement of the USD/TWD exchange rate and explore whether big data analytics with machine-learning modeling can exceed the random walk mechanism and the notion of market efficiency.
Abstract: The authors analyze relevant public information provided by new media to predict the movement of the USD/TWD exchange rate and explore whether big data analytics with machine-learning modeling can exceed the random walk mechanism and the notion of market efficiency.
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TL;DR: In this paper, the impact of economic news on the Lek exchange rate against two main hard currencies, Euro and US dollar, would serve to better orient the monetary policy and forex market agents positioning in time.
Abstract: Interpretation of exchange rate volatility in the light of economic fundamentals comprises an issue of interest for policymakers when it comes to implementing the monetary policy. Understanding the impact of economic news on the Lek exchange rate against two main hard currencies, Euro and US dollar, would serve to better orient the monetary policy and forex market agents positioning in time. Exchange rates volatility on economic news in short-term is an often discussed phenomenon in the economic literature, but through this material we tend to measure these effects in the Albanian foreign currency market and contribute in the literature interpreting foreign currency markets volatility in developing economies. Very often, domestic foreign exchange movements are attributed to developments in large international markets. In the case of Albanian Lek volatility analysis, we tend to find answers regarding the importance of economic news coming from the two main economies in focus, Eurozone and the US. Furthermore, we investigate the importance of the economic information flow in Albania in determining the Lek exchange rate against Euro and US dollar. For a period in focus from January 2007 until July 2012, we try to understand if the exchange rate volatility has been a result of economic fundamentals or financial markets stress related economic news. JEL classification: F31, F42. E52.