Impact of the COVID-19 Outbreak on the Currency Exchanges of Selected Countries

The paper aims to analyse the impact of the COVID outbreak on the currency market. The study considers spot rates of seven major currencies (i.e., EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CAD, USD/CHF, and CHF/JPY). To capture the impact of the outbreak on returns and the volatility of returns of seven currencies during pandemic, the study has segregated in two window periods (i.e., pre- [1st Jan 2019 to 31st Dec, 2019] and post-outbreak of COVID-19 [1st Jan, 2020 to 22nd Dec, 2020]). The study has applied various methods and models (i.e., econometric-based compounded annual growth rate [CAGR], dummy variable regression, and generalized autoregressive conditional heteroskedasticity [GARCH]). The result of the study captures the negative impact of the COVID-19 pandemic on three currencies—USD/JPY, AUD/USD, and USD/CHF—and positive significant impact on EUR/USD, GBP/USD, USD/CAD, and CHF/JPY. Investors can take short position in these while having long position in other currencies. The inferences drawn from the analysis are providing insight to investors and hedgers.

Is the volatility of international tourism revenues affected by tourism source market structure? An empirical analysis of Turkey

This study examines the effect of tourism source market structure on the volatility of tourism revenues in Turkey, using the number of tourists according to nationality and the data on international tourism revenues. The tourism source market structure was measured using the normalized Herfindahl–Hirschman index and the relative entropy index, which is based on the number of tourists visiting Turkey from 107 source markets. The volatility of tourism revenues and the effect of tourism source market structure on this volatility were assessed using the autoregressive conditional heteroskedasticity (ARCH) method. The results show that both variables measuring tourism source market structure affect the volatility of tourism revenues. Accordingly, the concentration of the tourism source market increases the volatility of tourism revenues, whereas source market diversification decreases it.

Are soft commodities markets affected by the Halloween effect?

Within the last three decades commodity markets, including soft commodities markets, have become more and more like financial markets. As a result, prices of commodities may exhibit similar patterns or anomalies as those observed in the behaviour of different financial assets. Their existence may cast doubts on the competitiveness and efficiency of commodity markets. It motivates us to conduct the research presented in this paper, aimed at examining the Halloween effect in the markets of basic soft commodities (cocoa, coffee, cotton, frozen concentrated orange juice, rubber and sugar) from 1999 to 2020. This long-time span ensures the credibility of results. Apart from performing the two-sample t-test and the rank-sum Wilcoxon test, we additionally investigate the autoregressive conditional heteroskedasticity (ARCH) effect. Its presence in our data allows us to estimate generalised autoregressive conditional heteroskedasticity [GARCH (1, 1)] models with dummies representing the Halloween effect. We also investigate the impact of the January effect on the Halloween effect. Results reveal the significant Halloween effect for cotton (driven by the January effect) and the significant reverse Halloween effect for sugar. It brings implications useful to the main actors in the market. They may apply trading strategies generating satisfactory profits or providing hedging against unfavourable changes in soft commodities prices.

Qualitative assessment of the complexity of the Karman vortex in the flow past double cylinders based on Shannon entropy

Numerical simulations of flows past double cylinders under the conditions of different inlet velocities are carried out based on finite element methods. The phenomenon of Karman vortex is observed in the numerical study. Shannon entropy of the velocity field is calculated to quantify the complexity of the velocity field, and the time-evolution of the Shannon entropy data is analyzed by time series models of ARMA (autoregressive moving average) and GARCH (generalized autoregressive conditional heteroskedasticity).

Is Bitcoin a safe haven? Application of FinTech to safeguard Australian stock markets

Purpose This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19. Design/methodology/approach The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021. Findings The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations. Originality/value There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.

Evolution of bitcoin as a Financial Asset

The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoin’s pricing at various cryptocurrency market development stages — before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) — the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets — stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.

Quantitative Modeling of Science and Technology Finance Supporting Industrial Innovation Development Based on Internet of Things Technology

Objectives: In recent years, science and technology financial support industries are actively supporting the innovation and development of high-tech industries. In order to test the actual effect of S&T financial support industry support plan, a GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model is designed by using K-means (K-means clustering) algorithm and GM (1,1) (grey prediction) algorithm, which can quantitatively display the development of S&T financial industry to promote high-tech. The GARCH model is used to quantify the degree of innovation and development of science and technology finance industry in the Internet of Things (loT) technology. Finally, according to the quantitative data obtained by GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model, the actual effect of science and technology finance industry promoting innovation and development of high-tech is evaluated by FAHP (Fuzzy Analytic Hierarchy Process) model. The results show that science and technology finance industry plays a positive role in promoting the innovation and development of loT technology.

Model ARIMA-GARCH Pada Peramalan Harga Saham PT. Jasa Marga (Persero)

Abstract– PT Jasa Marga is a great reputation company, the leader in comparable businesses, has a steady income, and paying dividends consistently. This paper aims to find the best model to forecast stock price of PT Jasa Marga using ARIMA-GARCH. The data used is daily stock price of PT Jasa Marga from March 2020 to March 2021. Autoregressive Integrated Moving Average (ARIMA) is a method that can be used to forecast stock prices. However, an economical data tend to have heteroscedasticity problems, one of the methods used to overcome them is Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Future stock price of PT Jasa Marga is forecasted with ARIMA-GARCH model. The data is modeled with ARIMA first, if there is heteroscedasticity, combine the model with GARCH model. The result of this study indicated that ARIMA (1, 1, 1) – GARCH (2, 2) is the best model, with MAPE 1,5647 Abstrak– PT Jasa Marga adalah perusahaan yang reputasinya baik, terdepan di perusahaan-perusahaan sejenis, stabil pendapatannya, dan pembayaran devidennya konsisten. Paper ini bertujuan untuk mencari model terbaik dalam meramalkan harga saham PT Jasa Marga menggunakan ARIMA-GARCH. Data harga saham yang diolah yaitu data sekunder dari PT Jasa Marga pada Maret 2020 hingga Maret 2021. Autoregressive Integrated Moving Average (ARIMA) sebagai metode yang dapat dimanfaatkan guna meramalkan harga saham. Akan tetapi, data tentang ekonomi cenderung memiliki masalah heteroskedastisitas, metode yang umum dipakai untuk mengatasinya adalah Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Harga saham PT Jasa Marga diramalkan dengan model ARIMA-GARCH. Data terlebih dahulu dimodelkan dengan ARIMA, jika didapati adanya heteroskedastisitas, maka model tersebut dikombinasikan dengan GARCH. Penelitian ini menghasilkan ARIMA (1,1,1)-GARCH(2,2) sebagai model terbaik dengan MAPE 1,5647.

A Robust Test for Monotonicity in Asset Returns

In this paper, we propose a robust test of monotonicity in asset returns that is valid under a general setting. We develop a test that allows for dependent data and is robust to conditional heteroskedasticity or heavy-tailed distributions of return differentials. Many postulated theories in economics and finance assume monotonic relationships between expected asset returns and certain underlying characteristics of an asset. Existing tests in literature fail to control the probability of a type 1 error or have low power under heavy-tailed distributions of return differentials. Monte Carlo simulations illustrate that our test statistic has a correct empirical size under all data-generating processes together with a similar power to other tests. Conversely, alternative tests are nonconservative under conditional heteroskedasticity or heavy-tailed distributions of return differentials. We also present an empirical application on the monotonicity of returns on various portfolios sorts that highlights the usefulness of our approach.

The Impact of the Introduction of Index Futures on the Daily Returns Anomaly in the Ho Chi Minh Stock Exchange

This study investigated the impact of the introduction of the VN30-Index futures contract on the daily returns anomaly for the Ho Chi Minh Stock Exchange (HOSE). Daily returns of the VN30-Index for the period 6 February 2012 through 31 December 2019 are used in this study to ascertain the new VN30-Index futures contract influence on the day-of-the-week anomaly observed in the HOSE. To test this effect, ordinary least square (OLS), generalized autoregressive conditional heteroskedasticity [GARCH (1,1)] and exponential generalized autoregressive conditional heteroskedasticity [EGARCH (1,1)] regression models were employed. The empirical results obtained from the models support the presence of the day-of-the-week effect for the HOSE during the study period. Specifically, a negative effect was observed for Monday. However, the analysis revealed that the day-of-the-week effect was only present in stock returns for the pre-index futures period, not for the post-index futures period. These findings suggest that the introduction of the VN30-Index futures contract had a significant impact on the daily returns anomaly in Vietnam’s HOSE, providing evidence that the introduction of the index futures contract facilitated market efficiency.