Showing papers on "Foreign exchange market published in 2021"
TL;DR: This work used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex, and proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data.
Abstract: Forex (foreign exchange) is a special financial market that entails both high risks and high profit opportunities for traders. It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies. However, incorrect predictions in Forex may cause much higher losses than in other typical financial markets.
The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems. In this work, we used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex. We utilized two different data sets—namely, macroeconomic data and technical indicator data—since in the financial world, fundamental and technical analysis are two main techniques, and they use those two data sets, respectively. Our proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data.
91 citations
TL;DR: In this article, the authors explored the hedging and safe-haven potential of green bonds for conventional equities, fixed income, commodity, and forex investments using a cross-quantilogram approach.
Abstract: Against the backdrop of the Covid-19 pandemic, this study explores the hedging and safe-haven potential of green bonds for conventional equity, fixed income, commodity, and forex investments We use the cross-quantilogram approach that provides a better understanding of the dynamic relationship between assets under different market conditions Our full sample results show that the green bond index could serve as a diversifier asset for medium- and long-term equity investors Besides, it can also serve as a hedging and safe haven instrument for currency and commodity investments Moreover, the sub-sample analysis of the pandemic crisis period shows a heightened short- and medium-term lead-lag association between the green bond index and conventional investment returns However, the green bond index emerges as a significant hedging and safe-haven asset for the long-term investors of conventional financial assets Our results offer insights for long-term investors whose portfolios comprise conventional assets such as equities, commodities, forex, and fixed income securities Further, our findings reveal the potential role that the green bond investments could play in global financial recovery efforts without compromising the low-carbon transition targets
54 citations
TL;DR: Wavelet Coherence Analysis was applied to examine the co-movements between markets in Iran in a time period from September 2014 to June 2020, as an intense period of uncertainty in Iran, and showed that the oil price had a low co- Movements with the other three markets, i.e. stock exchange, exchange rate, and gold markets.
42 citations
TL;DR: The empirical study shows that the DMC-EVT model outperforms the alternative copula models and confirms the existence of financial contagion in the forex market during the 2007-2009 global financial crisis, and finds that wealth constraints are the contagion channel during the crisis.
25 citations
TL;DR: In this article, the authors consider the effects of the recent COVID-19 pandemic event on the global equity market, commodities and FX market, measured in terms of the investors' fear index.
Abstract: Purpose: Market volatility is subject to good or bad news and even responses to fake news and policy changes. In this piece of work, the authors consider the effects of the recent COVID-19 pandemic event on the global equity market, commodities and FX market, measured in terms of the investors' fear index. Design/methodology/approach: In this empirical work, the authors employ time series-based regression models followed by augmented dummy regressions and growth of the COVID-19. Findings: COVID-19-induced investors' fear appears to be higher in the equity segment for the first time since the market crash of 1987 and the global financial crisis of 2008–2009. Furthermore, this disease outbreak shock has been more pronounced in terms of crude oil prices. Besides, a market participant in the commodity and FX market has paid a disproportionate premium to protect such pandemic development. Findings show that Options act as the best hedge against an uncertainty like COVID-19 and that option-based implied volatility is the best measure of investors' fear and market volatility. Practical implications: This study has practical implications for the financial markets, e.g. (1) Contagious disease outbreak news matters for the equity, commodity, and foreign exchange markets – empirical outcome validates the theory of market efficiency valid for the Options. (2) Option's implied volatility is the best indicator of investor fear measured for the unprecedented economic news. Further implication holds for the policymakers and society, e.g. (1) The unavailability of short-selling could be one plausible reason for increased uncertainty and volatility;hence, policymakers should look upon this issue at the exchange level. (2) Any market needs multiple lines of risk management, effective price discovery and attractive liquidity. Originality/value: The study is novel in terms of presenting market behavior amid COVID-19 across global equity markets and commodities and FX markets. © 2021, Emerald Publishing Limited.
21 citations
TL;DR: In this paper, the authors investigated whether bitcoin has a nonlinear relationship with six currencies: euro, pound sterling, Swiss franc, renminbi, yen and ruble, each denominated in US dollars.
17 citations
TL;DR: A combined technique based on ensemble multi-class support vector machine (EmcSVM) and fuzzy NSGA-II for efficient trend classification and trading in Forex markets and results show that the proposed method outperforms the existing crisp trading systems.
Abstract: Foreign exchange (Forex) market is the biggest currency exchange market in the world. Existing trading systems in Forex markets based on technical analysis use crisp technical indicators to provide Buy/Sell signals to the trader, only when the indicator value crosses a given threshold level. This strict and noise-sensitive condition can be replaced through uncertainty handling of indicators using fuzzy numbers to generate Buy/Sell signals with fuzzy memberships functions. To achieve this purpose, this paper presents a combined technique based on ensemble multi-class support vector machine (EmcSVM) and fuzzy NSGA-II for efficient trend classification and trading in Forex markets. At first, EmcSVM is used to forecast and classify the future market trend into uptrend, sideway, and downtrend. Then, NSGA-II is applied to optimize the hyperparameters of the proposed fuzzy trading system comprising multiple AND-OR Buy/Sell technical rules for uptrend/downtrend markets. The hyperparameters include indicator selection within each rule, importance weights of the different rules, and final decision thresholds for Buy/Sell models, while the objective is to maximize average return on investment (ROI) and minimize average draw-down of all transactions. The proposed method has been successfully developed and tested on real data from the Forex market for EUR/USD currency pair in a 6-year timeframe from 2014 to 2019. Obtained results show that the proposed method outperforms the existing crisp trading systems, with 80.8% precision, 72.4% recall, 94.1% annual ROI, and 0.58% draw down.
16 citations
TL;DR: In this paper, a quantitative model of exchange rates in which participants in the foreign exchange market are intermediaries subject to value-at-risk (VaR) constraints is proposed.
14 citations
TL;DR: In this paper, the authors examined the effect of asymmetric structure inherent in exchange rate volatility on trade in sub-Saharan African countries from 2005 to 2017 and found that the asymmetric components (positive and negative shocks) have negative and significant effect on trade.
Abstract: The purpose of this study is to examine the effect of asymmetric structure inherent in exchange rate volatility on trade in sub-Saharan African countries from 2005 to 2017.,17 countries in sub-Saharan African Countries are used for the study. Exchange rate volatility is generated using generalised autoregressive conditional heteroscedacity (1,1), while the asymmetric components of exchange rate volatility are generated using a refined approach of cumulative partial sum developed by Granger and Yoon (2002). Two-step generalised method of moments is used as the estimation technique in order to address the problem of endogeneity, commonly found in panel data.,The result from the study shows the evidence of exchange rate volatility clustering which is strictly persistent in sub-Saharan African countries. The asymmetric components (positive and negative shocks) of exchange rate volatility have negative and significant effect on trade in the region. Meanwhile, the effect of negative exchange rate volatility is higher on trade when compared with the positive exchange rate volatility. Furthermore, real exchange rate has negative and significant effect on trade in sub-Saharan African countries.,The outcomes of this study are important for participants in foreign exchange market. As investors in foreign exchange market react more to the negative news than positive news, investors need to diversify their risk. Also, regulators in the market need to formulate appropriate macroeconomic policies that will stabilize exchange rate in the region.,This study deviates from extant studies in the literature by incorporating asymmetric structure into the exchange rate trade nexus using a refined approach.
14 citations
TL;DR: In this paper, the effects of U.S. unconventional monetary policy announcements on the implied volatility of three major currency pairs, including the Dollar/Euro, Dollar/GBP, and Dollar/Yen, were studied.
Abstract: This paper studies the effects of U.S. unconventional monetary policy announcements on the implied volatility of three major currency pairs, Dollar/Euro, Dollar/British Pound and Dollar/Yen by usin...
13 citations
TL;DR: In this article, a complete trading system with a combination of trading rules on Forex time series data is developed and made available to the scientific community, which is implemented in two phases: In the first phase, each trading rule, both the AI-based rule and the trading rules from the technical indicators, is tested for selection; in the second phase, profitable rules are selected among the qualified rules and combined.
Abstract: The foreign exchange market (Forex) is the world’s largest market for trading foreign money, with a trading volume of over 5.1 trillion dollars per day. It is known to be very complicated and volatile. Technical analysis is the observation of past market movements with the aim of predicting future prices and dealing with the effects of market movements. A trading system is based on technical indicators derived from technical analysis. In our work, a complete trading system with a combination of trading rules on Forex time series data is developed and made available to the scientific community. The system is implemented in two phases: In the first phase, each trading rule, both the AI-based rule and the trading rules from the technical indicators, is tested for selection; in the second phase, profitable rules are selected among the qualified rules and combined. Training data is used in the training phase of the trading system. The proposed trading system was extensively trained and tested on historical data from 2010 to 2021. To determine the effectiveness of the proposed method, we also conducted experiments with datasets and methodologies used in recent work by Hernandez-Aguila et al. , 2021 and by Munkhdalai et al. , 2019. Our method outperforms all other methodologies for almost all Forex markets, with an average percentage gain of 20.2%. A particular focus was on training our AI-based rule with two different architectures: the first is a widely used convolutional network for image classification, i.e. ResNet50 ; the second is an attention-based network Vision Transformer (ViT). The results provide a clear answer to the main question that guided our research and which is the title of this paper.
TL;DR: This study attempts to analyse the applicability of machine learning techniques in predicting the currency exchange rate in a very short-term period particularly in the case of Indian Rupees Vs U.S Dollars (USD).
TL;DR: In this article, a network topology analysis is used to visualize the network connectedness between crude oil markets, foreign exchange markets, and the news sentiment on the most volatile days in 2020.
TL;DR: With the RMB becoming the fifth international payment currency and its inclusion in the SDR currency basket, coupled with the opening of China's capital market, RMB-related exchange rates have attr...
Abstract: With the RMB becoming the fifth international payment currency and its inclusion in the SDR currency basket, coupled with the opening of China’s capital market, RMB-related exchange rates have attr...
TL;DR: In this paper, the authors studied the impact of economic news releases of the United States on the tail risk of Mexican financial markets and found that news releases from the US and Mexico have a statistically significant impact on Mexican financial market.
TL;DR: In this paper, the authors investigated gold's role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions, and found that gold acts as a weak (strong) hedge for oil (currency) market movements, across all agent types.
Abstract: We investigate gold’s role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions. For the empirical analysis, we extend the intraday multifractal correlation measure developed by Madani et al. (Bankers, Markets & Investors, 163:2–13, 2020) to consider the dependence for calm and extreme movement periods across different time scales. Interestingly, we employ the rolling window method to examine the time-varying dependence between gold-oil and gold-currency in terms of calm and turmoil market conditions. Based on high frequency (5-min intervals) across the period 2017–2019, our analysis shows three interesting findings. First, gold acts as a weak (strong) hedge for oil (currency) market movements, across all agent types. Second, gold has strong safe-haven capability against extreme currency movements, and against only short time scales of oil price movements. Third, hedging strategies confirm the scale-dependent gold's role in reducing portfolio risk as a hedge or safe haven. Implications for investors, financial institutions, and policymakers are discussed.
TL;DR: In this paper, the authors established a direct link between (anti) herding behavior in currency markets and investor sentiment, proxied by a social media based investor happiness index built on Twitter feed data.
Abstract: This paper establishes a direct link between (anti) herding behavior in currency markets and investor sentiment, proxied by a social media based investor happiness index built on Twitter feed data....
23 Aug 2021
TL;DR: In this paper, the authors highlight the impact of COVID-19 confirmed cases and deaths occurring in the Middle East region on several economies, aiming to understand the importance of the ongoing effects of COVI-19 on several countries.
Abstract: By aiming to understand the importance of the ongoing effects of COVID-19 on several economies, this study attempts to highlight the impact of COVID-19 confirmed cases and deaths occurring in the m...
TL;DR: In this article, the authors present a heterogeneous agents model of the foreign exchange market in which agents' risk attitudes vary over time due to psychological factors emphasized in prospect theory, and find that psychological component and risk-profit elasticity play significant roles in exchange rate expectations formation and investment behavior.
TL;DR: In this article, the degree of connectedness of Asia Pacific forex markets post-global financial crisis and relates it to developments in the renminbi markets was investigated, and results from a two-step analysis involving a panel regression provide evidence that time-varying sensitivities of Asian Pacific currencies to the reninbi are directly related to the countries' trade and financial links with China.
Abstract: This paper investigates the degree of connectedness of Asia Pacific forex markets post‐global financial crisis and relates it to developments in the renminbi markets. The connectedness measure developed by Diebold and Yilmaz (2014) (Journal of Econometrics, 182, 119–134) reveal the strength of linkages across the U.S. dollar currency pairs of 12 currencies, namely offshore renminbi, onshore renminbi, euro, yen, Australian dollar, Indian rupee, Korean won, Malaysian ringgit, New Zealand dollar, Singapore dollar, Thai baht and Taiwan dollar. With the gradual liberalization of China's exchange rate system, shocks from the renminbi markets contribute more to fluctuations in almost all individual Asia Pacific currency markets vis‐a‐vis the yen. Rolling regressions show a number of Asia Pacific currencies exhibit tighter association with the renminbi when the latter underwent exchange rate regime reform. Finally, results from a two‐step analysis involving a panel regression provide evidence that time‐varying sensitivities of Asia Pacific currencies to the renminbi are directly related to the countries' trade and financial links with China.
TL;DR: In this paper, the authors investigate the magnitudes and determinants of volatility spillovers in the foreign exchange market, using realized measures of volatility and heterogeneous autoregressive (HAR) models.
TL;DR: The authors investigated the directional predictability of exchange rates in emerging markets using a cross-quantilogram model, and they showed that dependencies among emerging markets exchange rates are heterogeneous and that time-varying traits reveal time-variant traits.
TL;DR: In this article, the authors developed a model to explain a time-varying and currency-dependent interaction between stock prices and exchange rates, and concluded that the correlation between exchange rates and stock prices is determined by the relative sensitivity of two countries' stock prices to the common stock factor.
TL;DR: This paper examined the cross-sectional predictive ability of the Refinitiv Environmental, Social and Governance (ESG) score for returns in the foreign exchange market, using ESG scores aggregated at the national level, and found that ESG is a strong negative predictor of currency returns.
Abstract: We examine the cross-sectional predictive ability of the Refinitiv Environmental, Social and Governance (ESG) score for returns in the foreign exchange market, using ESG scores aggregated at the national level, and find that ESG is a strong negative predictor of currency returns. Intuitively, investors require a premium for financing low-ESG countries while high-ESG countries offer lower returns and provide a hedge in the bad state of the world. We show that ESG is priced in the cross-section of currency returns. We also consider the different components of ESG and show that its predictability is driven by the environmental pillar of the ESG ratings. Our results have strong implications for portfolios of individual investors and fund managers and offer new insights for policymakers in the foreign exchange market. The profitability of the ESG currency strategy is not driven by the carry trade and is robust to transaction costs.
TL;DR: In this paper, the determinants of stock return revelation in oil and gas mining division companies recorded on the Indonesia Stock Trade in 2019-2021 amid the Covid 19 emergency were analyzed.
Abstract: This study points to analyze the determinants of stock return revelation in oil and gas mining division companies recorded on the Indonesia Stock Trade-in 2019-2021 amid the Covid 19 emergency. The think about utilized the Eviews Program as information preparing and the irregular impact relapse show was chosen to look at the relationship between outside and inside markers as autonomous factors counting Current Ratio (CR), debt to equity ratio (DER), total asset turnover (TATO), return on assets (ROA), oil price (WTI), an exchange rate (FOREX), institutional ownership (IO). The comes about appeared that the current proportion, obligation to value ratio, and add up to resource turnover did not influence stock returns. Return on resources, exchange rates, and institutional ownership has a negative and significant impact on stock returns, while oil prices have a positive and widespread effect on stock returns.
Keywords: Oil Price, Stock Return, Probability, Institutional Ownership, Covid-19
TL;DR: In this paper, the authors find that the volatility of the US dollar-Thai baht exchange rate has a positive effect on market participation measured by trading volume and average transaction size.
Abstract: Existing models of market participation offer contrasting predictions on the impact of asset price volatility on market participation. Utilising granular trading data from the Thai foreign exchange (FX) market, we test the empirical relevance of these predictions. We find that the volatility of the US dollar–Thai baht exchange rate has a positive effect on market participation measured by trading volume and average transaction size. This finding is consistent with the models illustrating that volatility increases participation as it creates profit-making opportunities. The result is robust to controlling for information flow that may generate a positive but non-causal relationship between volatility and participation. We also find heterogeneity across participant types. In particular, the impact of FX volatility on trading volume is positive for foreign end-customers and interbank players, but negative for local end-customers. This heterogeneity is explained by different purposes of FX transactions: financial returns for the former and real demand for the latter. Finally, we show that the impact of volatility on trading volume turns negative at high levels of volatility and during a period of high regulatory uncertainty.
02 Aug 2021
TL;DR: In this article, the authors consider the task of the analysis, modeling, and application of dependencies between asset quotes at various capital markets and provide a theoretical basis to asset management strategies that estimate an asset's price via regression, taking into account its correlated assets in various markets.
Abstract: In this paper, we consider the task of the analysis, modeling, and application of dependencies between asset quotes at various capital markets. As an example, we study the dependency between financial instrument observation series in the currency and stock markets. Our work intends to give a theoretical basis to asset management strategies that estimate an asset’s price via regression, taking into account its correlated assets in various markets. Furthermore, we provide a way to increase the estimate quality using an evolutionary algorithm.
06 Oct 2021
TL;DR: In this paper, the authors investigated the volatility impact of crude oil and gold on interest rates and contributed to the existing literature with its findings, but there is no evidence of volatility spillover from gold and crude oil on the interest rates.
Abstract: Crude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.,This study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.,The main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.,Gold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.,Gold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.
TL;DR: This paper investigates how classification algorithms can be incorporated in the process of predicting trend reversals to create DC‐based trading strategies and shows that the introduction of classification leads to return higher profit and statistically outperform all other trading strategies.
Abstract: The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets. Using physical time scales can make companies oblivious to significant activities in the market as the flow of time is discontinuous, which could translate to missed profitable opportunities or risk exposure. Directional Changes (DC) has gained attention in the recent years by translating physical time series to event-based series. Under this framework, trend reversals can be predicted by using the length of events. Having this knowledge allows traders to take an action before such reversals happen and thus increase their profitability. In this paper, we investigate how classification algorithms can be incorporated in the process of predicting trend reversals to create DC-based trading strategies. The effect of the proposed trend reversal estimation is measured on 20 foreign exchange markets over a 10-month period in a total of 1,000 datasets. We compare our results across 16 algorithms, both DC and non-DC based, such as technical analysis and buy-and-hold. Our findings show that the introduction of classification leads to return higher profit and statistically outperform all other trading strategies.