scholarly journals On Tail Dependence and Multifractality

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1767
Author(s):  
Krenar Avdulaj ◽  
Ladislav Kristoufek
Keyword(s):  
Financial Markets ◽  
Tail Dependence ◽  
Autoregressive Process ◽  
Gaussian Copula ◽  
Monte Carlo Simulation Study ◽  
Auto Correlation ◽  
Correlation Structures ◽  
Auto Regressive ◽  
Generalized Hurst Exponent ◽  
Multifractal Dynamics

We study whether, and if yes then how, a varying auto-correlation structure in different parts of distributions is reflected in the multifractal properties of a dynamic process. Utilizing the quantile autoregressive process with Gaussian copula using three popular estimators of the generalized Hurst exponent, our Monte Carlo simulation study shows that such dynamics translate into multifractal dynamics of the generated series. The tail-dependence of the auto-correlations forms strong enough non-linear dependencies to be reflected in the estimated multifractal spectra and separated from the case of the standard auto-regressive process. With a quick empirical example from financial markets, we argue that the interaction is more important for the asymmetric tail dependence. In addition, we discuss and explain the often reported paradox of higher multifractality of shuffled series compared to the original financial series. In short, the quantile-dependent auto-correlation structures qualify as sources of multifractality and they are worth further theoretical examination.

scholarly journals Tail dependence of the Gaussian copula revisited

2016 ◽  
Vol 69 ◽  
pp. 97-103 ◽  
Author(s):  
Edward Furman ◽  
Alexey Kuznetsov ◽  
Jianxi Su ◽  
Ričardas Zitikis
Keyword(s):  
Tail Dependence ◽  
Gaussian Copula

Spatial auto‐correlation and auto‐regressive models estimation from sample survey data

2020 ◽  
Vol 62 (6) ◽  
pp. 1494-1507
Author(s):  
Roberto Benedetti ◽  
Thomas Suesse ◽  
Federica Piersimoni
Keyword(s):  
Survey Data ◽  
Sample Survey ◽  
Auto Correlation ◽  
Sample Survey Data ◽  
Auto Regressive

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

2019 ◽  
Vol 20 (4) ◽  
pp. 962-980 ◽  
Author(s):  
Shegorika Rajwani ◽  
Dilip Kumar
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Tail Dependence ◽  
Time Varying ◽  
The Us ◽  
Asian Economies ◽  
Copula Approach

During the past few years, many of the financial markets have gone through devastating effects due to the crisis in one or the other economy of the world. The recent global financial crisis has triggered dramatic movements in various stock markets which may arise from interdependence or contagion between the markets. This article attempts to measure the contagion between the equity markets of Asia and the US stock market. The countries considered in the Asian group are China, India, Indonesia, South Korea, Taiwan, Hong Kong, Malaysia and Japan. Most of the Asian economies have experienced drastic higher volatility and uncertainty in the financial markets. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of the portfolio. In such a case, the policymakers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises. To achieve our goal, we make use of the time-varying copula approach which helps us to study the joint behaviour of the series based on their marginal distribution. Time-varying copula approach can also capture the non-linear dependence in the series and exhibits a rich pattern of tail behaviour. Our findings support the contagion between the Asian stock markets and the US stock market during the global financial crisis. This article also highlights that the increased tail dependence is an important factor for the contagion between the Asian stock markets and the US market.

scholarly journals Correlation Structures of PM2.5 Concentration Series in the Korean Peninsula

2019 ◽  
Vol 9 (24) ◽  
pp. 5441
Author(s):  
Gyuchang Lim ◽  
Seungsik Min
Keyword(s):  
Cross Correlation ◽  
Moving Average ◽  
Air Pollutant ◽  
Fluctuation Analysis ◽  
Auto Correlation ◽  
Cross Correlation Analysis ◽  
Correlation Structures ◽  
Spatio Temporal ◽  
Pm2.5 Concentration ◽  
Mass Concentrations

In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM2.5 (particulate matter of diameter less than 2.5 μ m ) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM2.5 daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM2.5 concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.

scholarly journals White Noise Tests on the LSTM Model Trained with Double Pendulum

2021 ◽  
Vol 2137 (1) ◽  
pp. 012032
Author(s):  
Xisen Wang
Keyword(s):  
Time Series ◽  
White Noise ◽  
Time Series Data ◽  
Initial Conditions ◽  
Correlation Coefficients ◽  
Series Data ◽  
Double Pendulum ◽  
Auto Correlation ◽  
State Dependent ◽  
Auto Regressive

Abstract This paper describes the intrinsic qualities of a simple double pendulum (DP), with a visual representation, a rigorous deduction of the Lagrangian equation, and a concrete factor analysis. LSTM model was utilized to simulate the double pendulum’s periodic and chaotic behaviors and evaluates the effectiveness of the model. The auto-correlation coefficients was calculated. Meanwhile, Box-Pierce test and Ljung-Box tests for various state-dependent time series were conducted to give various initial conditions to explore the DP system’s random characteristics. The research results are as follows: 1) Chaos did not lead to direct randomness; 2) seasonality could coexist with chaos; 3) the highly auto-regressive nature of DP’s time series data are found. Therefore, it can be concluded that the chaos in a double pendulum has particular patterns (such as the positive relationship with the likelihood of being a random white noise series) that could be further explored.

scholarly journals Modelling Joint Behaviour of Asset Prices Using Stochastic Correlation

2020 ◽  
Author(s):  
László Márkus ◽  
Ashish Kumar
Keyword(s):  
Stock Prices ◽  
Goodness Of Fit ◽  
Tail Dependence ◽  
Heston Model ◽  
Gaussian Copula ◽  
Suitable Model ◽  
Stochastic Correlation ◽  
Wiener Processes ◽  
Correlation Models ◽  
Joint Behaviour

Abstract Association or interdependence of two stock prices is analyzed, and selection criteria for a suitable model developed in the present paper. The association is generated by stochastic correlation, given by a stochastic differential equation (SDE), creating interdependent Wiener processes. These, in turn, drive the SDEs in the Heston model for stock prices. To choose from possible stochastic correlation models, two goodness-of-fit procedures are proposed based on the copula of Wiener increments. One uses the confidence domain for the centered Kendall function, and the other relies on strong and weak tail dependence. The constant correlation model and two different stochastic correlation models, given by Jacobi and hyperbolic tangent transformation of Ornstein-Uhlenbeck (HtanOU) processes, are compared by analyzing daily close prices for Apple and Microsoft stocks. The constant correlation, i.e., the Gaussian copula model, is unanimously rejected by the methods, but all other two are acceptable at a 95% confidence level. The analysis also reveals that even for Wiener processes, stochastic correlation can create tail dependence, unlike constant correlation, which results in multivariate normal distributions and hence zero tail dependence. Hence models with stochastic correlation are suitable to describe more dangerous situations in terms of correlation risk.

ARE FINANCIAL INVESTORS PRONE TO EXOGENOUS (CRICKET) SENTIMENTS THAT AFFECTS EQUITY INVESTMENT DECISION AND INDUCES VOLATILITY IN STOCK MARKET?

2020 ◽  
Vol 38 (2) ◽  
Author(s):  
Muhammad Kashif ◽  
Asra Shaikh ◽  
Mobeen Ur Rehman
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Investment Decision ◽  
Negative Influence ◽  
Bad News ◽  
Equity Investment ◽  
Auto Correlation ◽  
Asymmetric Behavior ◽  
Auto Regressive ◽  
The Impact

This paper examines the volatility in stock returns due to mood-swings of financial investors affected by the outcome of one-day international (ODI) cricket matches played by Pakistan against cricketing nations. The impact of matches is analyzed on same-day and for next-day volatility in returns by using Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH 1,1) and Glosten, Jagannathan & Runkle (GJR 1,1) methodology, supported by Engle (arch), L-Jung Q-stats (auto-correlation) and Jarque-Bera (normality) tests. Empirical time-series results show volatility can be predicted through past volatility and can be generalized. The win or loss position of Pakistan in ODI has a significant influence on next day volatility of stock returns. However, GJR analysis provides strong evidence of asymmetric behavior on next day in Karachi Stock Exchange (KSE)-100 index, states bad-news resulting from ODI matches has a significant negative influence on the next-day volatility of stock returns, due to less trading on the subsequent day of the match.

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