Volatility Movements Research Articles

EXCHANGE RATE FLUCTUATION AND PERFORMANCE OF MANUFACTURING COMPANIES IN NIGERIA

Exchange rate fluctuation poses a significant threat to the stability of a nation's economy. Every economy must strive to ensure stability in exchange rate to strengthen the purchasing power. The volatile movement of exchange rate in Nigeria induces increase in cost which adversely affects the manufacturing sector. Hence, this study investigated the effect of exchange rate fluctuation on performance of manufacturing sector in Nigeria. Auto Regressive Distributed Lag was used to analyse the secondary data that were collected from Central Bank statistical bulletin and annual reports. The study revealed that there is existence of a long run negative relationship between exchange rate fluctuation and contribution of manufacturing sector to GDP. There is also existence of a long run negative relationship amongst exchange rate fluctuation and manufacturing output in Nigeria. Finally, the study revealed that exchange rate fluctuation affects manufacturing capacity utilization in Nigeria. The study concluded that exchange rate volatility negatively affects Nigeria manufacturing sector. The study therefore recommends that macro-economic variables are known to affect performance of manufacturing sector therefore the monetary policies should be managed in a way that it will be favourable to manufacturing sector in Nigeria.

The MAPE Analysis of Arima (p,d,q) on LQ45 Stock Price to Determine Training Data Period

Most of the research using the Arima (p,d,q) focused on the accuracy of prediction results. Unlike other research, this work examines the training data period suitable for modeling ARIMA (p,d,q) in stock prices. Due to the volatile movement of stocks, the number of training data is assumed to affect the LQ45 prediction results. This research used five kinds of training data, including daily data for up to 5 years. With these five types of data series, the Arima (p,d,q) was made for LQ45 stocks. The prediction was conducted for two months after obtaining the model 5 data series of LQ45 stocks. Two months of data were used for January and February 2021 prediction test data. The results of this prediction were compared with the test data to produce the MAPE value. Based on the observations and calculation results, the most suitable stock to use the Arima (p,d,q) was ASII. In 5 years, the stocks produced the lowest MAPE value of 0.05%. Relatively stable LQ45 stocks with no change in the Arima (p,d,q) using four consecutive data series were ACES, CTRA, INTP, MIKA, and TLKM. Based on the MAPE value analysis performed in this study, we concluded that the best period to use the Arima (p,d,q) for LQ45 stocks is two years, with a median error rate of only 6.0091%.

Underplating-induced trans-crustal melting and maturation of Neoarchean continental crust in the North China Craton

A global change in granitoid compositions from early predominantly sodic tonalite-trondhjemite-granodiorites (TTGs) to later TTGs and more potassic granites occurred during the late Archean, coupled with a major period of crustal maturation. However, the detailed relationship between granitoid chemical evolution and the maturing crustal process remains enigmatic. Successive granitoid magmatism including late Neoarchean TTGs and high-K granites occurred in the Western Shandong Province granite-greenstone belt (WSP) of the North China Craton and thus preserves crucial clues of the crustal maturation process. In this study, petrology, whole-rock geochemistry, and zircon U-Pb and Lu-Hf isotopes are reported for the late Neoarchean TTG gneisses, monzogranites, and minor metabasaltic to andesitic rocks from the WSP. The ca. 2560−2540 Ma TTG gneisses show low MgO, K2O/Na2O, but high (La/Yb)N, Sr/Y, and absence of Eu anomalies, indicating their derivation from partial melting of the thickened lower mafic crust. The ca. 2530−2500 Ma monzogranites are characterized by systematically high SiO2 and K2O/Na2O, but low MgO and Sr/Y, and moderately negative Eu anomalies, revealing they were formed by intracrustal reworking of local TTGs and sedimentary rocks in the middle to upper crust. Geochemical variations of these crustal-derived granitoids suggest that they were formed by melting at gradually higher crustal levels with the melt zone moved gradationally from the eclogite stability field into the plagioclase stability field. The ca. 2530−2500 Ma calc-alkaline metabasaltic to andesitic rocks sourced from metasomatized mantle outline roles of mantle-derived magma underplating in contributions of heating and trans-crustal melting magmatism. The long-term melting processes facilitated the upward movement of volatiles and heat-producing elements from deep to shallow crustal levels, and introduced K-enriched monzogranites into the upper crust, leaving a refractory, strengthening, and tectonically stable lower crust. Secular compositional evolution of crustal-derived granitoids reveals that continuous crustal reworking drove lithosphere differentiation and paved the way for the maturation of the Archean continental crust.

Deep learning enhanced volatility modeling with covariates

Exogenous information such as policy news and economic indicators can have the potential to trigger significant movements in financial asset volatility. This article presents a model, called the RECH-X model, that allows incorporating exogenous variables into a recurrent neural network for volatility modeling and forecasting. The RECH-X model can allow for abrupt changes in the volatility level and effectively capture the complex serial dependence structure in the volatility dynamics. We demonstrate in a wide range of applications that the RECH-X model consistently outperforms the benchmark models in terms of volatility modeling and forecasting.

A multiscale analysis of returns and volatility spillovers in cryptocurrency markets: A post-COVID perspective

ABSTRACT Since the onset of the COVID-19 pandemic, leading cryptocurrencies have undergone significant price fluctuations, prompting widespread interest in the interdependence and spillover effects among cryptocurrency markets, as well as in identifying the key cryptocurrencies that drive market movements. This study contributes to the existing literature by utilising innovative vector wavelet coherence (VWC) and wavelet local multiple correlation (WLMC) frameworks to investigate the time-frequency co-movements among four cryptocurrencies (Bitcoin, Ethereum, Tether, and Binance). By exploring the co-movements across multiple time scales over a period from 01/01/2020 to 10/04/2023 through continuous and discrete wavelet coherency analysis, we identify four key empirical findings. Firstly, the returns connectedness is stronger than the volatility connectedness. Secondly, high-frequency co-movements are more erratic and correspond to positive and negative unexpected news, while low-frequency co-movements vary with changes in US monetary policy. Thirdly, Tether and Binance exhibit the weakest returns and volatility connectedness with other cryptocurrencies. Lastly, Ethereum and Tether (not Bitcoin) are the primary cryptocurrencies that account for returns and volatility movements in the market. We discuss the implications of these findings for various stakeholders in cryptocurrency markets.

Indonesia Sharia Stock Investment During Covid-19: Based on Islamic Economic Law Review

The development of technology and financial literacy has increased the number of Indonesians who use stocks as an investment instrument. This also has an impact on the popularity of Islamic stocks. This is because many people are interested in Islamic investment instruments. However, the COVID-19 pandemic has had a significant impact on stock price movements. Sharia stock prices have also experienced very volatile movements due to the COVID-19 pandemic. This phenomenon raises the question of whether investing in Islamic stocks during the COVID-19 pandemic is against Islamic economic law. This is because Islamic stock prices seem to be filled with uncertainty and have experienced a very significant price decline. Moreover, due to the COVID-19 pandemic, the number of stock investors is increasing rapidly. This article aims to analyze Islamic stock investment during the COVID-19 pandemic through the perspective of Islamic economic law. To analyze this issue, this article collects data through library research. The data were analyzed using qualitative methods. In the end, this article finds that investing in Islamic stocks during the COVID-19 pandemic does not contradict the principles of Islamic economic law. Instead, this article suggests that the public can take advantage of a certain momentum to start investing in Islamic stocks.

Optimization investment portfolio of ESG capital market in Indonesia: An investigation of Polynomial Goal Programming based on higher moments

The development of capital market investors in Indonesia has experienced rapid growth, leading to increasingly volatile movements in the domestic market. Companies or investment managers must form asset portfolios with optimal expected returns and measurable risks to address this issue. This study aims to analyze the potential combinations that can be formed using ESG Capital Market Indonesia 2022 and safe-haven instruments, utilizing the Markowitz theory and Polynomial Goal Programming (PGP) method. This research uses secondary data, and the research variables used are ESG Capital Market Indonesia 2022 and Gold from January 2007 to December 2022. Based on the previous research, the formation of portfolio combinations using safe-haven instruments, employing the Markowitz theory and Polynomial Goal Programming (PGP) method can provide better investment portfolio combinations during crises. Furthermore, the performance of each formed combination is measured using the Sharpe Ratio. The findings indicate that Markowitz's theory can generate better performance than the PGP method. However, it exhibits suboptimal skewness and kurtosis values compared to the PGP method.

COVID-19 pandemic and stock market volatility spillovers

Purpose This paper aims to explore the impact of the coronavirus on the volatility spillovers of 10 selected developed markets hit by this pandemic (e.g. the USA, Canada, Korea, Japan, the UK, Germany, Italy, Spain, France and China). Design/methodology/approach The database consists of daily data from January 1, 2020, to December 31, 2022. The data used are the precise daily closing prices of various indices of selected markets gathered from the DataStream and Investing.com databases. The authors use the VAR model to study the transmission of volatility between stock markets and analyze the dynamic links between them. Then, the Granger causality test is used to study the volatility movements and determine which of these markets is likely to influence the others. Then, impulse response functions are used to understand the reactions of the studied markets following shocks in the two most important markets, namely, the American and Chinese markets. Finally, forecast errors variance decomposition is used to measure the dynamic interactions that characterize the relationships between the studied markets. Findings Empirical results reveal instability in the returns of various indexes and the existence of causal relationships between standardized volatility of markets. The reactions of some markets following a shock in American and Chinese markets differ among markets. The empirical results also show that forecast errors variance of some markets begin coming from their own innovations during first periods. These shares decrease then in favor of other markets interventions. Practical implications The findings have significant practical implications for governments around the world as well as for financial investors. The successful practice of China’s pandemic prevention and control efforts may inspire governments to determine how to overcome panic and strengthen confidence in victory. Policymakers can use the insights from our study to design more effective economic policies and regulations to mitigate the negative impact of future pandemics on the financial system. Regulators can use these results to identify areas of weakness in the financial system and take proactive measures to address them. Financial investors may use the outcomes of our result to better understand the impact of global pandemics on financial markets. They may know which markets are the most active, which ones are causing considerable effects on the others and which ones show resilience and an anti-risk capacity. This may help them to make appropriate decisions about their investments. Originality/value It has become imperative to estimate the impact of this pandemic on the behavior of financial markets to prevent the deterioration and dysfunction of the global financial system. The findings have important implications for financial investors and governments who should know which markets are the most shaken, which cause remarkable effects on others and which show resilience and anti-risk capacity. Countries could follow China in some measures taken to moderate the negative effects of this epidemic on national economies.

Comparison of LSTM and GRU Models for Forex Prediction

Trading foreign currencies worth trillions of dollars takes place daily in the forex market, characterized by highly volatile movements. The forex market operates on bid and ask prices, with exchange rates determined by the principles of supply and demand. Trading involves currency pairs like EUR/USD, where the value of the Euro is compared to the US Dollar, serving as a basis for analyzing price fluctuations. Due to the volatile nature of forex, market participants must make informed decisions when buying and selling, as improper choices can result in financial losses. One approach to mitigating risk in forex trading decisions is through the use of forecasting techniques. This research study employs LSTM and GRU methods to predict forex trends, which are evaluated using various dataset divisions. The most accurate results are obtained using a dataset of 4979, split into three equal parts: 80% for training, 10% for validation, and 10% for testing. This approach yields an RMSE value of 0.054, MAPE of 0.037, and R-square of 97%

Prediksi Harga Forex Menggunakan Algoritma Long Short-Term Memory

Trillions of dollars per day of foreign currency trading activity occur in the forex market, which has very volatile movements in foreign currency trading. Trade based on bid and ask prices. The market determines foreign exchange rates based on supply and demand rules. Currency trading in pairs such as EUR/USD is a comparison of the value of the Euro against the Dollar as a basis for research, rising and falling currency prices in forex move fluctuatingly, so a market participant must be able to decide on buying and selling positions. Because wrong decisions can lead to losses. One of the ways to reduce risk in making decisions in buying and selling in forex can be using forecasting. This study uses the LSTM method in predicting forex prices which will be tested on several scales of dataset distribution. The smallest error results using a total dataset of 2631 with a dataset division of 70:15:15, which is divided into 70% data for training, 15% data for validation and 15% data for testing produces an RMSE value of 0.038, MAPE 2.5%. In measuring how well the regression model used with Rsquare on the data distribution is 70:15:15 and the total dataset used is 4979 to get the best results, namely 97%.

Interconnectedness of Cryptocurrency Uncertainty Indices with Returns and Volatility in Financial Assets during COVID-19

This paper investigates the dynamic relationship between cryptocurrency uncertainty indices and the movements in returns and volatility across spectrum of financial assets, comprising cryptocurrencies, precious metals, green bonds, and soft commodities. It employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach; the analysis covers both the entire sample period spanning August 2015 to 31 December 2021 and the distinct phase of COVID-19 pandemic. The findings of the study reveal the interconnectedness of returns within these asset classes during the COVID-19 pandemic. In this context, cryptocurrency uncertainty indices emerge as influential transmitters of shocks to other financial asset categories and it significantly escalates throughout the crisis period. Additionally, the outcomes of the study imply that during times of heightened uncertainty, exemplified by events such as the COVID-19 pandemic, the feasibility of portfolio diversification for investors might be constrained. Consequently, the amplified linkages between financial assets through both forward and backward connections could potentially compromise financial stability. This research sheds light on the impact of cryptocurrency uncertainty on the broader financial market, particularly during periods of crisis. The findings have implications for investors and policymakers, emphasizing the need for a comprehensive understanding of the interconnectedness of financial assets and the potential risks associated with increased interdependence. By recognizing these dynamics, stakeholders can make informed decisions to enhance financial stability and manage portfolio risk effectively.

A new hybrid PM[Formula: see text] volatility forecasting model based on EMD and machine learning algorithms.

In recent years, the frequent occurrence of air pollution incidents has seriously affected people's health and life. Therefore, PM[Formula: see text], as the main pollutant, is an important research object of air pollution at present. Effectively improving the prediction accuracy of PM[Formula: see text] volatility makes the PM[Formula: see text] prediction content perfect, which is an important aspect of PM[Formula: see text] concentration research. The volatility series has an inherent complex function law, which drives the volatility movement. When machine learning algorithms such as LSTM (Long Short-Term Memory Network) and SVM (Support Vector Machine) are used for volatility analysis, a high-order nonlinear form is used to fit the functional law of the volatility series, but the time-frequency information of the volatility has not been utilized. Based on EMD (Empirical Mode Decomposition) technique, GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model and machine learning algorithms, a new hybrid PM[Formula: see text] volatility prediction model is proposed in this study. This model realizes time-frequency characteristic extraction of volatility series through EMD technology, and integrates residual and historical volatility information through GARCH model. The simulation results of the proposed model are verified by comparing the samples of 54 cities in North China with the benchmark models. The experimental results in Beijing showed that MAE (mean absolute deviation) of hybrid-LSTM decreased from 0.00875 to 0.00718 compared with LSTM, and hybrid-SVM based on the basic model SVM also significantly improved generalization ability, and its IA (index of agreement) improved from 0.846707 to 0.96595, showing the best performance. The experimental results show that the hybrid model is superior to other considered models in terms of prediction accuracy and stability, which verifies that the hybrid system modeling method is suitable for PM[Formula: see text] volatility analysis.

Lifetime asset allocation with long run risk and time various risk aversion

We propose in this article an optimal lifetime asset allocation model in which we simultaneously consider two important stylized facts, long-run risk and time-varying risk aversion. Our model also accounts for the uncertainty and correlation of long-run returns and volatility. Based on American Consumer Expenditure Survey data and macroeconomic data, we have three main findings. First, individual’s risk attitude roughly rises with age, and it has a significant effect on the individual’s best asset allocation choice. Second, two pairs of correlations between the movement of returns and volatility impact an individual’s choice. In the long run, individuals may take the comovement of returns and volatility into account and adjust their optimal investment strategy. The effects of the adjustment term are determined by the direction and strength of the correlations. Third, the economy has a clear business cycle path, and the optimal fraction of risky assets in portfolio vary within a wide range when the risk premium is high during one’s lifetime. We further, based on our data and parameters, perform a simulation to identify the path of the optimal investment strategy. The result implies that individuals and households with lifetime investments such as pension funds should take risk preferences variation into account, and this is especially for older individuals.

Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model

The impact of cryptocurrency on other assets has become a subject of intense research, given the rise of digital currency over the last decade. However, unlike traditional assets, cryptocurrency has been subject to extreme movements in price and volatility. As a result, it has become important for investors and risk managers to model and forecast volatility and correlation between digital currency and other assets. This paper utilises a multivariate generalised autoregressive score (GAS) model to study the time-varying dependence between stock prices (S&P500, NASDAQ, Dow Jones Industrial) and cryptocurrencies (Bitcoin and Ethereum). The results show that the GAS framework outperforms the traditional DCC-GARCH model, capturing the volatility persistence and non-linearity between stock and cryptocurrency. Regarding the correlations, while we identify a time-varying relationship, the strength of this relationship is in the low-to-moderate range. In addition, our forecasting exercise shows that the GAS specification has superior forecasting ability beyond certain horizon days compared to the DCC-GARCH model.

Dynamic Conditional Correlation and Volatility Spillover between Conventional and Islamic Stock Markets: Evidence from Developed and Emerging Countries

This study aims to investigate the dynamic conditional correlation and volatility spillover between the conventional and Islamic stock markets in developed and emerging countries in order to develop better portfolio and asset allocation strategies. We used both multivariate GARCH (MGARCH) and multi-scales-based maximal overlap discrete wavelet transform (MODWT) approaches to investigate dynamic conditional correlation and volatility spillover between conventional and Islamic stock markets in developed and emerging countries. The results show that conventional and Islamic markets move together in the long run for a specific time horizon and present time-varying volatility and dynamic conditional correlation, while volatility movement changes due to financial catastrophes and market conditions. Further, the findings point out that Chinese conventional and Islamic stock indexes showed higher volatility, whereas Malaysian conventional and Islamic stock indexes showed comparatively lower volatility during the global financial crisis. This study provides fresh insights and practical implications for risk management, asset allocation, and portfolio diversification strategies that evaluate stock market reactions to the crisis in the international avenues of finance literature.

Long memory cointegration and dynamic connectedness of volatility in US dollar exchange rates, with FOREX portfolio investment strategy

<abstract> <p>Decisions of central banks on foreign exchange rates are based on the comovement of foreign exchange (FOREX) in mature markets such as US dollar rates to the British pound, euro, Chinese yuan, Japanese yen and Australian dollar. We investigate the long-run movement and dynamic quantile connectedness of volatility among pairs of these exchange rates. The updated residual-based fractional cointegration testing framework using narrow-band frequency domain least squares estimator is used to obtain the residual series for fractional cointegration. Quantile dynamic connectedness framework for volatility spillovers at different market conditions, depicted by quantiles, are used. We find evidence of long memory cointegration in seven pairs of exchange rates involving the previously mentioned currencies. These seven cases also correspond to a higher average index of quantile connectedness, with the effect of connectedness phasing out at higher quantiles and being more visible at lower quantiles. A portfolio investment strategy using optimal portfolio weights and hedge ratios for maintaining the accrued profit at the FOREX market is also presented.</p> </abstract>

The Characteristics of Cryptocurrency Market Volatility: Empirical Study For Five Cryptocurrency

In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions.
 This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.

Predicting The Stock Closing Price of ISX-Listed Companies Using LSTM

Financial markets are highly reactive to events and situations, as seen by the very volatile movement of stock values. As a result, investors are having difficulties guessing prices and making investment decisions, especially when statistical techniques have failed to model historical prices. This paper aims to propose an RNNs-based predictive model using the LSTM model for predicting the closing price of four stocks listed on the Iraq Stock Exchange (ISX). The data used are historical closing prices provided by ISX for the period from 2/1/2019 to 24/12/2020. Several attempts were conducted to improve models training and minimize the prediction error, as models were evaluated using MSE, RMSE, and R2. The models performed high accuracy in predicting closing price movement, despite the Intense volatility of time series. The empirical study concluded the possibility of relying on the RNN-LSTM model in predicting close prices at the ISX as well as decisions making upon.

EMPIRICAL ANALYSIS OF THE VOLATILITY EFFECT OF THE COVID-19 PANDEMIC PROCESS ON NATURAL GAS FUTURE TRANSACTIONS IN TURKEY

The aim of this study is to investigate the volatility movements in natural gas returns, which is one of the financial investment instruments in futures markets, before and after the Covid-19 pandemic, using GARCH family models. For this purpose, daily data from 30.08.2017 to 10.03.2020 before the Covid-19 Pandemic, and daily data from 11.03.2020 to 21.09.2021 after the Covid-19 Pandemic were used. The return on natural gas futures before the Covid-19 Pandemic was expressed as RLNPO and the return on natural gas futures after the Covid-19 Pandemic was expressed as RLNPS. For RLNPO, TGARCH was determined as the most suitable volatility model according to Schwarz Information Criteria, and EGARCH was determined as the most suitable volatility model for RLNPS. As a result of these analyzes, it has been seen that natural gas futures returns can be explained by asymmetric volatility models before and after the Covid-19 Pandemic, but there is no leverage effect as a result of asymmetric volatility, and positive shock asymmetries have a greater effect on volatility. The asymmetric effect tends to decrease in the post-Covid-19 Pandemic period.

Is Tether a safe haven of safe haven amid COVID-19? An assessment against Bitcoin and oil using improved measures of risk

Bitcoin is a new speculative investment with extremely volatile movement, thus possibly failing to act as a safe haven for crude oil when the price of this energy commodity plummeted following the global outbreak of COVID-19. Meanwhile, Tether is designed to behave similarly to the US dollar with stable fluctuation. In this study, we assessed their safe-haven properties in terms of risk reduction opportunities by proposing an improved version of Value-at-Risk (VaR) and Expected Shortfall (ES). Using vine copula-based AR-GJR-GARCH models, we demonstrated that Bitcoin exhibited inconsistent risk reduction capability for oil, particularly before COVID-19. When adding Tether into a portfolio containing oil and Bitcoin, the risk reduction was achieved for any portfolio allocation and was more pronounced amid the COVID-19 period. This suggests that Tether consistently served strong support for Bitcoin to protect oil investors against extreme risk and received a significant impact from the COVID-19 outbreak. However, the consistent safe-haven functionality of Tether was not as good as that of the US dollar in most cases, and this implied the vanishing of its stability. These results were robust when considering another asymmetric volatility model and another dependence model. Furthermore, the proposed improved VaR and ES forecasts outperformed their corresponding unimproved version in quantifying portfolio risk and therefore provided a more accurate assessment of safe-haven roles.