A Comparative Study of Financial Crises: Fractal Dissection of Investor Rationality

2021 ◽  
pp. 097226292110225
Author(s):  
Sonali Agarwal ◽  
Anshul Vats
Keyword(s):  
Time Series ◽  
Stock Markets ◽  
Time Series Data ◽  
Series Data ◽  
Normal Functioning ◽  
Research Article ◽  
Dimension Analysis ◽  
Fractal Dimension Analysis ◽  
Gains And Losses ◽  
Measurable Change

Any non-linear dynamic system can be checked for structural properties only at the time of extremes/crises. Hence, in this research article we tried to investigate stock markets for visible patterns or structures in the vicinity of crashes. We used fractal dimension analysis for studying the volatility of prices and presence of noise and patterns in the time series data of NIFTY, SENSEX and gold. We found change in market predictability of the various time series in the surrounding of crash points. There was measurable change in persistence levels around rupture points. It can be concluded that excessive order in stock markets can choke the markets which then witness crashes to relieve this symmetry and resume randomness for normal functioning. We supported the results with behavioural biases and patterns of investors. The repetitive trading psychology, different intensity of emotions of investors towards their gains and losses, and onset of irrationality and fear leads to worsening of any financial crisis. The crashes can have devastating effects on the economy and the investors. We there have tried to find visible patterns that can serve as warning signals of an approaching crisis. This can be of special assistance to the investors, traders and speculators who enjoy playing in the stock market.

Can the Artificial Heart Make the Circulation Become Fractal?

1995 ◽  
Vol 18 (4) ◽  
pp. 190-196 ◽  
Author(s):  
T. Yambe ◽  
S. Nanka ◽  
S. Naganuma ◽  
S. Kobayashi ◽  
H. Akiho ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Time Series ◽  
Fractal Dimension ◽  
Artificial Heart ◽  
Time Series Data ◽  
Series Data ◽  
Hemodynamic Parameters ◽  
Arterial Blood ◽  
Dimension Analysis ◽  
Fractal Dimension Analysis

In order to analyze the hemodynamic parameters in prosthetic circulation as an entity and not as decomposed parts, non linear mathematical analyzing techniques, including the fractal dimension analyzing theory, were utilized. Two pneumatically actuated ventricular assist devices were implanted, as biventricular bypasses (BVB), in chronic animal experiments, using four healthy adult goats. For the comparison between the natural and prosthetic circulation in the same animals, the BVB type complete prosthetic circulation model with ventricular fibrillation, was adopted. All hemodynamic parameters with natural and prosthetic circulation were recorded under awake conditions, and calculated with a personal computer system. Using the non-linear mathematical technique, the arterial blood pressure waveform was embedded into the return map as the beat-to-beat time series data and fractal dimension analysis were performed to analyze the reconstructed attractor. By the use of the Box counting method, fractal dimension analysis of the hemodynamics was performed. Return map of the hemodynamics during natural and artificial circulation showed fractal characteristics, and fractal dimension analysis of the arterial blood pressure revealed the fact that lower dimensional fractal dynamics were evident during prosthetic circulation. Fractal time series data is suggested to have robustness and error resistance, thus our results suggest that the circulatory regulatory system with an artificial heart may have these desired characteristics.

scholarly journals Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shanglei Chai ◽  
Zhen Zhang ◽  
Mo Du ◽  
Lei Jiang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Spillover Effects ◽  
Series Data ◽  
Dominant Source ◽  
Financial Internationalization

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

ANALISIS HUBUNGAN ANTARA HARGA MINYAK, PASAR SAHAM, DAN NILAI TUKAR DI INDONESIA

2019 ◽  
Vol 1 (4) ◽  
Author(s):  
Nadia Kurnianti ◽  
Idris Idris
Keyword(s):  
Time Series ◽  
Exchange Rates ◽  
Stock Markets ◽  
Time Series Data ◽  
Oil Prices ◽  
Series Data ◽  
Var Model ◽  
Causality Relationship ◽  
Relationship Of ◽  
The Relationship

The aim of this research is to analyze the relationship of causality between oil prices, stocks market, and exchange rates in Indonesia using VAR model. The data used in this study is time series data from January 2014 until December 2018 that was obtained from the relevant institutions. The variables use are oil prices (X1), stocks market (X2), and exchange rates (X3). The method used in this study is Vector Auto Reggression (VAR). The finding has shown that there are no causality relationship between the oil prices, stock markets, and exchanger rates. The finding also shown that there is only directional relationship between exchange rates with stocks market.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

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.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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