scholarly journals Detection of Outliers in the Volatility of Malaysia Shariah Compliant Index Return: The Impulse Indicator Saturation Approach

2020 ◽  
pp. 1-7
Author(s):  
Ida Normaya Mohd Nasir ◽  
Mohd Tahir Ismail
Keyword(s):  
Time Series Data ◽  
Global Financial Crisis ◽  
Financial Time Series ◽  
The United States ◽  
Series Data ◽  
Crude Oil Prices ◽  
Global Events ◽  
Using Data ◽  
Impulse Indicator Saturation ◽  
Detection Of Outliers

Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS).Monte Carlo simulations illustrate the ability of IIS to detect outliers by using data with various simulation settings. For empirical application, we have chosen the Malaysia Shariah compliant index which is the FBM EMAS Shariah (FBMS) index. The result of this study discovered the presence of 47 outliers which related to several global events such as global financial crisis (2008 & 2009), the falling of stock market (2011), the United States debt-ceiling crisis (2013) and the declination of international crude oil prices (2014). Keywords: outliers; volatility; stock indices; IIS

scholarly journals ANALISIS PERDAGANGAN JAGUNG INDONESIA

Agro Ekonomi ◽  
2010 ◽  
Vol 17 (2) ◽  
Author(s):  
Muhammad Imam Ma'ruf
Keyword(s):  
United States ◽  
Comparative Advantage ◽  
Time Series Data ◽  
The United States ◽  
Ordinary Least Squares ◽  
Raw Material ◽  
Series Data ◽  
Causality Test ◽  
Integration Test ◽  
Using Data

Corn has a strategic role and economic value in Indonesia, and has to be developed due to its position as the main source of carbohydrate and protein, raw material for food, feed, and biofuel industry. Aimed this research to determine the position of Indonesian com competitiveness in the international market in know the comparative advantage of Indonesian corn;factors that influence Indonesian com demand, and the integration between Indonesian corn market and the world com market. This research applys descriptive method The data used are time series data sourced from FAO, National Statistic Agency (BPS), and World Bank. Competitiveness is measured by the parameters of Revealed Comparative Advantage, Trade Specialist Ratio, Acceleration Ratio, and Market Penetration Index. The RCA, TSR, and AR analysis used data year 1988-2008, the MPI analysis used data year 1995-2008. Indonesian import corn demand is analyzed by OLS (ordinary least squares) multiple regression in the form of natural logarithm using data year 1980-2008, while market integration is analyzed by the unit root test, co-integration test, and Granger causality test using data year 1961-2008. The results shows that 1) the Indonesian corn competitiveness is low caused by the low production of Indonesian com; 2) Indonesian import corn demand is positively affected by the price of imported com and GDP of Indonesia, and negatively affected by the price of imported soybean and imported rice. Imported soybeans are complements of cornfor feed, while imported rice is the substitute of com for feed,' 3) There is no integration between the Chinese market and the Indonesian market because China is a country which re-export corn, there is integration between the United States market and the Indonesian market, as well as between Argentina's market and Indonesian market, but there is no causal relationship. The United States and Argentina market is not a dominant (leading) market in pricing of Indonesian Corn.market.

scholarly journals Analisis Daya Saing Ekspor Komoditi Kopi (Hs 090111) Indonesia Di Pasar Amerika Serikat : Pendekatan RSCA dan CMS

2021 ◽  
Vol 6 (3) ◽  
pp. 83
Author(s):  
Nurul Fatimah ◽  
Ignatia Martha H ◽  
Kiki Asmara
Keyword(s):  
United States ◽  
Quantitative Research ◽  
Time Series Data ◽  
Secondary Data ◽  
The United States ◽  
Series Data ◽  
Competitive Edge ◽  
Constant Market Share ◽  
And Performance ◽  
Using Data

Indonesia is one of the largest coffee exporting countries in the United States market after Brazil, Colombia, Vietnam, and Guatemala. It is still unable to shift the export of coffee commodities from these four countries. This research aims to analyze the competitiveness and performance of coffee exports in the United States market using data analysis methods such as Revealed Symmetric Comparative Advantage (RSCA) and Constant Market Share (CMS). Research is classified as quantitative research that utilizes secondary data, an annual time series data, namely 2010-2019. The data source is exported data for Indonesian coffee commodity digit 6 with HS 090111 (Coffee, not roasted, not decaffeinated) obtained from the International Trade Center (ITC). This study's value results indicate that RSCA Indonesia is 0.87, where the RSCA is> 0. This shows that Indonesia still has competitiveness, although it is lower than Brazil0.95, Colombia, 0.96, and Guatemala, 0.97, and Indonesia is still superior to Vietnam, which is equivalent. 0.79. Meanwhile, the CMS value states that the Indonesian coffee commodity is less desirable in the United States market with an average commodity composition effect value of -0.00006. However, an increase in demand for Indonesian coffee commodities with an average market distribution effect value of 0.00002 and commodity Indonesian coffee has a competitive edge. Strong in the US market with an average competitiveness affect rating of 0.00001.

scholarly journals Analyzing Impact of a Crisis on Bank Financial Ratios

2020 ◽  
Vol 9 (4) ◽  
pp. 17
Author(s):  
Osman Nal ◽  
Andrew Cai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Financial Time Series ◽  
Financial Ratios ◽  
Series Data ◽  
Slope Parameter ◽  
Shift Parameter ◽  
Crisis Period ◽  
Capital Ratios

In this study we provide a practical framework and methodology for analyzing the effects of banking shocks (economic or financial in nature) on bank fundamentals, that avoids the use of complicated econometrics methods. For this, we focus our attention to the effects of the 2007-2008 global financial crisis on the four largest US banks and examine the variation of trends in the select financial ratios for those institutions using quarterly regulatory data running from 2002-Q4 to 2020-Q2. We start by plotting time series charts of those financial ratios for each bank and compare the before-crisis, transition and after-crisis periods. For this, we simply fit trend lines with three parameters of shift, slope, and volatility to the banking data. The shift parameter describes the level change of the variable when before- and after-crisis periods are compared. The slope parameter pronounces the difference in steepness of the trend lines, while the volatility parameter is associated with all three periods and describe the variation in the data during each period. Our results indicate that capital ratios, an important regulatory financial ratio, are higher across the board in the after-crisis period compared to before-crisis period, suggesting a positive shift. We don’t see significant changes in slope parameter for the capital ratio series leading us to suggest the use of dummy variable regression model where slope is treated as a fixed constant. We further show that pre-crisis and transition periods are characterized by higher volatilities that ultimately subside in the after-crisis period. Lastly, we conclude by suggesting that financial practitioners use the shift, slope and volatility parameters in understanding trends in financial time series data since it is easy to implement and interpret the results compared to more sophisticated econometric models.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

scholarly journals Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Vol 18 (9) ◽  
pp. 4560
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

scholarly journals An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

scholarly journals The 2010 Structural-Demographic Forecast for the 2010–2020 Decade: A Retrospective Assessment

2020 ◽  
Author(s):  
Peter Turchin ◽  
Andrey Korotayev
Keyword(s):  
Western Europe ◽  
Time Series Data ◽  
Political Instability ◽  
The United States ◽  
Stress Index ◽  
Series Data ◽  
Strongly Correlated ◽  
Retrospective Assessment ◽  
The United Kingdom ◽  
The Usa

This article revisits the prediction, made in 2010, that the 2010–2020 decade would likely be a period of growing instability in the United States and Western Europe (Turchin 2010). This prediction was based on a computational model that quantified in the USA such structural-demographic forces for instability as popular immiseration, intraelite competition, and state weakness prior to 2010. Using these trends as inputs, the model calculated and projected forward in time the Political Stress Index, which in the past was strongly correlated with socio-political instability. Ortmans et al. (2017) conducted a similar structural-demographic study for the United Kingdom and obtained similar results. Here we use the Cross-National Time-Series Data Archive for the US, UK, and Western European countries to assess these structural-demographic predictions. We find that such measures of socio-political instability as anti-government demonstrations and riots increased dramatically during the 2010–2020 decade in all of these countries.

scholarly journals Financial innovation and economic growth: evidence from Zimbabwe

2016 ◽  
Vol 13 (2) ◽  
pp. 65-75 ◽  
Author(s):  
Alex Bara ◽  
Calvin Mudzingiri
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Time Series Data ◽  
Financial Time Series ◽  
Financial Innovation ◽  
Dark Side ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Causality Tests

The role of financial innovation on economic growth in developing countries has not been actively pursued. Stemming from the finance-growth nexus, literature suggests that financial innovation has a relationship to growth, which could be either positive or negative. Implicitly, financial innovation has a good and a dark side that affects growth. This study establishes the causal relationship between financial innovation and economic growth in Zimbabwe empirically. Using the Autoregressive Distributed Lag (ARDL) bounds tests and Granger causality tests on financial time series data of Zimbabwe for the period 1980-2013, the study finds that financial innovation has a relationship to economic growth that varies depending on the variable used to measure financial innovation. A long-run, growth-driven financial innovationis confirmed, with causality running from economic growth to financial innovation. Bi-directional causality also exists after conditionally netting-off financial development. Policies that enhance economic growth inter-twined with financial innovation are essential, if developing countries, such as Zimbabwe, aim to maximize economic development

Factors Determining Gold Prices in Malaysia

2019 ◽  
Vol 1 (2) ◽  
pp. 75-82
Author(s):  
Anis Mat Dalam ◽  
Noorhaslinda Kulub Abd Rashid ◽  
Jaharudin Padli
Keyword(s):  
Crude Oil ◽  
Time Series Data ◽  
Oil Prices ◽  
Series Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Crude Oil Prices ◽  
Inflation Rates ◽  
Ardl Approach ◽  
Gold Reserve

Gold is a valuable asset to a country because of its liquidity. Gold reserve can stabilize the currency in a country. The objective of this paper is to identify the factors contributing to the volatility of gold prices, such as Real Malaysia GDP, inflation rates, crude oil prices and exchange rates. The data was analysed using Autoregressive Distributed Lag (ARDL) approach with time series data, with 30-year coverage from 1987 to 2016. Findings showed that only Real Malaysia GDP and crude oil prices were significantly related to gold prices. As a conclusion, this study can be used as reference by other investors. The author suggests to other researchers to further improve upon this study by adding more variables or diversifying the variables that relate to volatility of gold prices.

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