Future Exchange Research Articles

Implied volatility estimation of bitcoin options and the stylized facts of option pricing

The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging. The need for these tools dates back to the market crash of 1987, when investors needed better ways to protect their portfolios through option insurance. These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively. The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile, smirk, or skew in options markets. These stylized facts; that is, the volatility smile and implied volatilities implied by the option prices, are well documented in the option literature for almost all financial markets. These are expected to be true for Bitcoin options as well. The data sets for the study are based on short-dated Bitcoin options (14-day maturity) of two time periods traded on Deribit Bitcoin Futures and Options Exchange, a Netherlands-based cryptocurrency derivative exchange. The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis. This study has two aims: (1) to provide insights into the volatility smile in Bitcoin options and (2) to estimate the implied volatility of Bitcoin options through numerical approximation techniques, specifically the Newton Raphson and Bisection methods. The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data. Moreover, the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options. However, the Newton Raphson forecasting technique converges faster than does the Bisection method.

Prosecutorial Discretion in Tackling the Cryptocurrency Crime in Indonesia

Data, reports, and information show that cryptocurrency has supported certain parties as a convenience, whereas the purpose of cryptocurrency is to minimize the weaknesses of conventional systems in international relations in the current era of globalization. Countries that cannot represent or apply autonomous law in facing cryptocurrency challenges, because it is feared it is increasingly difficult to overcome global cryptocurrency crime. It is precisely in eradicating cryptocurrency crime, law enforcement authorities, priorities of prosecutors who have the highest supremacy in the field of prosecution and other discretion in law enforcement must be dynamic in law enforcement against facilitators from responses to social needs and aspirations, in accordance with legal considerations must acknowledge the wishes of the community and agree in achieving substantive justice. Considering that virtual currency has been banned in Indonesia but crypto-asset trading on the futures exchange has been in force, responsive discretionary prosecutions are needed in combating cryptocurrency crime in Indonesia. Liability that exceeds liability based on faults, namely strict liability, vicarious liability, and secondary liability to any parties that cause cryptocurrency crime can be applied to the mechanism of follow the money and trace the information and communication technologies (ICTs) footprint. It is hoped that prosecution discretion by the prosecutor can reach to the monitoring of suspicious nodes and monitoring the registration of ICTs that are vulnerable to cryptocurrency crimes, such as laptops, cellphones, computers, and SIM cards, in providing a deterrent effect to the perpetrators of cryptocurrency crime.

EXCHANGE RATE FORECASTING USING FUZZY TIME SERIES-MARKOV CHAIN

Exchange rate forecasting plays an important role in financial management. However, it is a complex process with high nonlinearity and data irregularity. Moreover, the forecasting of exchange rate is highly involved with imprecise and uncertain data. Analysis of forecasting models which corresponds to the exchange rate has always experienced fluctuations. Therefore, exchange rate forecasting becomes a challenging task in finance. Several studies have shown that stand-alone forecasting models such as time series, fuzzy time series, and Markov chain have their own drawbacks and are not successful enough in forecasting accurately. In this study, we propose a hybrid model of fuzzy time series-Markov chain to forecast the future exchange rate. Fuzzy time series-Markov chain is a combination of the classic fuzzy time series model with Markov chain model used to analyse a set of time series data. The main motivation for this study is to improve the accuracy in exchange rate forecasting. The selected currencies are Malaysian Ringgit (MYR) and Singapore Dollar (SGD). The proposed model was then evaluated by the Mean Absolute Percentage Error (MAPE) performance metric to test the robustness of the model. Lastly, a comparison between the proposed model and fuzzy time series model was conducted with respect to the MAPE. The results showed that the MAPE value for fuzzy time series-Markov chain was 0.9895% which fell under the criterion of highly accurate forecasting. Meanwhile, the MAPE value for fuzzy time series was 3.4306%. Thus, the forecasting performance of the proposed model was better than the fuzzy time series model. This study reveals the potential benefits of the proposed model as a highly accurate forecasting model.

Adapted Autoregressive Model and Volatility Model with Application

Price limits are applied to control risks in various futures mar kets. In this research, we proposed an adapted autoregressive model for the observed futures return by introducing dummy variables that represent limit moves. We also proposed a stochastic volatility model with dummy variables. These two models are used to investigate the existence of price de layed discovery effect and volatility spillover effect from price limits. We give an empirical study of the impact of price limits on copper and natural rubble futures in Shanghai Futures Exchange (SHFE) by using MCMC method. It is found that price limits are efficient in controlling copper futures price, but the rubber futures price is distorted significantly. This implies that the effects of price limits are significant for products with large fluctuation and frequent limits hit.

Export pricing and exchange rate expectations under uncertainty

This paper contributes to the literature on firms’ export pricing by assessing whether and to what extent firms take into account the expected future evolution of the exchange rates while setting their prices. Using French micro-level trade data, our empirical analysis reveals that by adjusting their export prices, firms partly absorb information about future exchange rate variations. The extent to which individual exporters absorb future exchange rate fluctuations is found to depend on their market power, in accordance with theoretical dynamic demand-side models encompassing mechanisms creating an inter-temporal relationship between current market shares and future profits. The analysis also shows that the strength of such expectation-related mechanism is considerably reduced with greater future exchange rate uncertainty, in line with an interpretation of pricing-to-market as an investment decision under uncertainty. In a comparative perspective our results are shown to drive asymmetric responses across destinations of aggregate bilateral export flows to expected exchange rate movements.

Classification of the IDR-USD Exchange Rate with Multilayer Perceptron Based on Detection Rate

Artificial neural network (ANN) is a set of units in processing a model based on the habits of human neural networks. ANN has one of its duties, namely classification with the concept of supervised learning. ANN also has various methods in performing its duties such as Multilayer perceptron (MLP). Where MLP is one of the ANN methods that can classify based on data as conceptualized in data mining. Very useful classifications and trends in the field of research because of the review of data that will generate knowledge. Nominal Exchange Rate is one of the datasets tested in this study. The exchange rate of the Indonesian Rupiah (IDR) against the United States Dollar (USD) is very necessary both in terms of stock movements and other businesses. So that, it is necessary to use classification to predict future exchange rates. In this study, the MLP method was carried out by obtaining a validation test using MAPE based on the detection rate of sebesar 0.500079879%.

Night trading with futures in China: The case of Aluminum and Copper

We use high-frequency data to examine the effects of introducing a night trading session at the Shanghai Futures Exchange (SHFE) in 2013. For Copper, the realized volatility of the regular session is endogenously determined, while the night session is driven by the immediately preceding volatility of the London Metal Exchange (LME). In contrast, Chinese Aluminum futures are more resistant to exogenous factors and show pronounced long memory. We find no indications that the SHFE draws volume from LME. The existing break between daytime and night session has significant informational content and must be separated when processing intraday data.

The future of cryptocurrency legality in Indonesia

Cryptocurrency is a form of payment for goods and services that can be made online. Many companies have issued their own currency, often called tokens, and traded specifically for the goods or services their company provides. Currently, blockchain technology can be implemented in various elements, including, financial services, smart property, IoT, smart contracts, blockchain government, blockchain identity. In Indonesia, cryptocurrencies are interpreted as crypto assets that can only be traded on futures exchanges. There are 229 cryptocurrencies recognized in Indonesia. The legality of using Cryptocurrencies in Indonesia can only be traded on futures exchanges, as stated in the regulation of the Commodity Futures Trading Supervisory Agency (BAPPEBTI) number 5 of 2019 and the Commodity Futures Trading Supervisory Agency Regulation Number 7 concerning the Establishment of a List of Crypto Assets that can be Traded in the Physical Crypto Asset Market.

Cross-commodity hedging for illiquid futures: Evidence from China's base metal futures market

This paper evaluates the effectiveness of cross-commodity hedging between China's base metal spot and futures markets, using daily data of metal spot and futures prices in the Shanghai Futures Exchange. The main findings suggest that, compared to unhedged spot portfolios, a naïve hedge increases risk exposure, while static and dynamic hedges can significantly reduce the risk of holding spot assets. Zinc futures and nickel futures outperform other base metal futures in individually hedging lead spot and tin spot respectively, while copper futures constitute a moderately optimal instrument to hedge both lead and tin spot assets.

Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information

A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines.

The interrelationship between order flow, exchange rate, and the role of American economic news

The aim of this study is to investigate the comovement and interconnection between US economic news in seven different categories and the exchange rate of the US dollar against the currencies of seven developed countries, with the corresponding order flows. Our empirical investigation is based on using the Spearman correlation method to analyze the correlation between the economic news and exchange rate using order flows before, during, and after the financial news announcement. We use wavelet transform analysis to assess the comovement in the time–frequency domains. The bivariate outcomes demonstrate that changes in economic news impact price changes for only a short time, or even no time at all. Moreover, for longer time periods, the order flow is shown to have stronger coherency than economic news and a steady comovement against the exchange rate. Wavelet coherency reveals that the lead-lag effect of order flow changes on price changes starts in the medium frequency bands to low-frequency bands during the entire sample period. In addition, economic news weakens the correlation between exchange rate and order flow in the short run, which means that the combination of economic news and order flow can achieve a higher degree of consistency of exchange rate. The implication of this study is that macro policy makers, import and export enterprises, foreign exchange investors and exchange fund managers can predict the future exchange rate based on the order flow and choose hedging measures under different objectives.

The Ebb and Flow of Study Abroad: A Comparative Analysis of PRC and International Students in Taiwan

The dialogue about study abroad is a contemporary trend. Since 2011, enrolments from the People’s Republic of China (PRC) have gradually increased and PRC students have now become one of the largest groups of incoming study abroad participants in Taiwan. In this study, investigators explored the characteristics of PRC students in comparison with other international students studying in Taiwan universities. Data were collected from 1870 study abroad students. Data collected include the various study abroad goals, prior study abroad experiences, the Short-term Study Abroad Situational Change Survey, the revised East Asian Acculturation Measures, the Study Abroad Acculturative Hassles, and their overall study abroad satisfaction. Data analyses included computation of the mean, frequency, cross-tabulation of respondents’ responses for identified questions, and various group comparisons. Implications suggest that the characteristics of PRC students are valuable and potentially transformative markers for sustainable cross-strait ties. Study abroad programs in Taiwan are noted as one piece of the emerging discourse for sustainable co-existence between Taiwan and the PRC. As such, PRC study abroad participants along with faculty and students in Taiwan universities have an opportunity to play a role in reshaping future exchanges as well as transforming themselves into stewards of a trans-Pacific community.

Efficiency of Tanzania's foreign exchange market

AbstractThis paper examines market efficiency in the foreign exchange market in Tanzania. A nonlinear Markov switching model was used to analyse the Tanzanian shilling against the US dollar spanning from 2 January 2009 to 30 April 2020. The estimation period was 2 January 2009–28 May 2019, and the post‐sample forecast period was 28 May 2019–30 April 2020. From the results of the two‐state Markov switching model, we define State 1, which is more volatile, as the inefficient state and State 2, which is less volatile, as the efficient one. In the unconditional probabilities from the Markov switching model, the exchange rate has a 94% probability in the inefficient state, which indicates that 94% of all speculators fully extract information when predicting future exchange rates, while 30% of all investors do not; suggesting that the foreign exchange market undergoes conditions of efficiency and inefficiency. In addition, findings showed that the exchange rate is expected to spend approximately 18 days in regime 1 and 8 days in regime 2. The policy implication of these findings is that foreign exchange market participants should pay attention to regimes and underlying market conditions when designing and applying their investment strategies.

Regionalism and bilateral counter-terrorism cooperation: the case of India and Thailand

ABSTRACT After major terrorist attacks in the United States on September 11, 2001, Bali, Indonesia in 2002, and Mumbai, India in 2008, India and Thailand responded in disparate ways. Thailand was slow to respond out of fear of alienating the large Muslim population in its restive south. India, after Mumbai, moved to create and strengthen domestic institutions that would hasten counter-terrorism measures, with limited success. In the years that followed these seminal events, both Bangkok and New Delhi have, like other South Asian and ASEAN states, moved toward bilateral and multilateral counter-terrorism partnerships. This paper surveys the history of terrorism in both countries and their domestic responses, persistent domestic and regional challenges, and documents a growing India-Thailand bilateral counter-terrorism partnership – particularly in the sectors of maritime security, intelligence sharing, extradition, and joint security cooperation. This paper contributes to a growing body of literature on emerging South-South partnerships by evaluating India-Thailand counter-terrorism cooperation and explores avenues for future bilateral exchanges and multilateral opportunities within institutional bodies, such as ASEAN and BIMSTEC.

What Determines a Country's Current Account and Exchange Rate? - a Tale of Two External Drivers

To make a meaningful response logic to future exchange rate pressures, we monitor the determinants of the current account and exchange rate. First, according to the analysis of current account determinants, the current account imbalance will gradually ease from a long-term and structural perspective if the domestic financial market develops or access to the international financial market is strengthened. In addition, existing structural current account estimates that do not take into account international financial market conditions may have underestimated the structural current account balance of emerging countries such as Korea. Second, based on the analysis of the currency determinants, we can argue that the weak won is structurally inevitable due to the weak yuan.

Foreign asset returns under exchange rate uncertainty: A classroom experiment

This paper provides details on a classroom experiment that focuses on returns to foreign assets given uncertain future exchange rates. Students are assigned the role of foreign analysts and decide how much to invest abroad given their expectations about future exchange rates. The experiment allows students to practice calculating the returns to foreign investments, understand the benefits to skill in predicting future exchange rates, and observe the limitations to that skill. The experiment is ideal for undergraduate students in macroeconomics or international economics courses in a classroom that would allow students to separate into distinct groups.

Investment and Margin Risk Analysis of Robusta Coffee Futures Contract at Jakarta Futures Exchange

Futures trading is developing rapidly in various countries and has become one of the supports for economic growth. However, studies on investment in futures contracts in Indonesia, particularly in measuring risk are still limited. This study aims to calculate the maximum potential loss on investment and the estimated margin on robusta coffee futures at JFX using Value at Risk (VaR). The analysis used symmetrical and asymmetric GARCH (Exponential-GARCH, Threshold-GARCH, and GJR-GARCH). It used data of robusta coffee price, covering spot prices, March contract prices, and September contract prices for the 2016-2019 period. The result of the study indicates that the GARCH (1,1) is the most suitable model for the three robusta coffee prices. Based on the VaR value, the investment risk in the robusta spot is higher than the robusta contract. The margin value interval that may be applied to JFX robusta coffee based on spot price analysis, March contracts, and September contracts reach 2.39%-2.91%.

Novel information fusion model for simulating the effect of global public events on the Sino-US soybean futures market

Trade frictions and global public health security events have made it more difficult for investors to generate positive returns from the Sino-US soybean futures markets. This paper employed deep learning and mode decomposition to improve market efficiency and reduce investor risk from Sino-US trade frictions and the COVID-19 pandemic using soybean futures data published on the Dalian Commodity Futures Exchange (DCE) and the Chicago Board of Trade (CBOT). The proposed model was found to assist investors to proactively perceive the market risks from disruptive events and make profitable decisions. The results provide practical guidance for the conduct of quantitative trading on the soybean markets between the two countries.

Value at risk estimation of the SET50 index: Comparison between stock exchange of Thailand and Thailand futures exchange

Value at risk estimation of the SET50 index: Comparison between stock exchange of Thailand and Thailand futures exchange

A Risk Measurement Model of China’s Non-Ferrous Metal Futures Market

With the impact of foreign exchange markets, risks in financial markets are becoming more complex and diversified, which underlines the importance of risk management in financial supervision. In this paper, China’s non-ferrous metal futures market is selected as the research object, and Shanghai Futures Exchange’s Industrial Metal Commodity Index (IMCI) data are used to measure risk using the conditional autoregressive value at risk (CAViaR) model. The US dollar index (USDX) is incorporated into the CAViaR model to study its impact on the risk. Through empirical analysis, we arrive at the following conclusions: First, the asymmetric slope CAViaR model (AS-CAViaR) is more suitable for measuring the risk in China’s non-ferrous metal futures market. Second, the risk is positively impacted by the lagged risk. Moreover, the impacts of positive and negative returns on the risk are asymmetric, with a negative return having a greater impact. Third, the positive and negative shock of USDX has significant and different impacts on the risk. These impacts can be caused by global capital flows. In addition, the impact of the vector of explanatory variables on the IMCI at different quantile levels is discussed based on the CAViaR-USDX model, which reflects the comprehensive advantages of the quantile regression method and the model’s applicability. The above conclusions verify the impact of USDX on China’s non-ferrous metal futures market and provide a theoretical basis and direction for risk monitoring.