Relationship research between meteorological disasters and stock markets based on a multifractal detrending moving average algorithm

2018 ◽  
Vol 32 (01) ◽  
pp. 1750267 ◽  
Author(s):  
Qingchen Li ◽  
Guangxi Cao ◽  
Wei Xu
Keyword(s):  
Financial Markets ◽  
Long Memory ◽  
Stock Markets ◽  
Moving Average ◽  
Preliminary Investigation ◽  
Autoregressive Integrated Moving Average ◽  
Auto Correlation ◽  
The Us ◽  
Sample Interval ◽  
Meteorological Disasters

Based on a multifractal detrending moving average algorithm (MFDMA), this study uses the fractionally autoregressive integrated moving average process (ARFIMA) to demonstrate the effectiveness of MFDMA in the detection of auto-correlation at different sample lengths and to simulate some artificial time series with the same length as the actual sample interval. We analyze the effect of predictable and unpredictable meteorological disasters on the US and Chinese stock markets and the degree of long memory in different sectors. Furthermore, we conduct a preliminary investigation to determine whether the fluctuations of financial markets caused by meteorological disasters are derived from the normal evolution of the financial system itself or not. We also propose several reasonable recommendations.

An Analysis of Long Memory in Volatility for Asian Stock Markets

2000 ◽  
Vol 03 (03) ◽  
pp. 309-330 ◽  
Author(s):  
Huimin Chung ◽  
William T. Lin ◽  
Soushan Wu
Keyword(s):  
Financial Markets ◽  
Memory Effect ◽  
Long Memory ◽  
Stock Markets ◽  
Asia Pacific ◽  
Size Distortion ◽  
Asset Return ◽  
Semiparametric Method ◽  
Aggregation Size ◽  
And Shifts

One of the important questions in studies of asset return and volatility has been how long the effects of shocks persist. In this article, the modified R/S statistic of Lo (1991) and the robust semiparametric method of Lobato and Robinson (1997) are applied to investigate the long memory properties in return and volatility of Asian financial markets. For the return series, we find little evidence of long memory, while the empirical results support the hypothesis of long memory in volatility for Asia-Pacific stock markets. We also discuss the possible causes of spurious long memory effect in volatility, namely aggregation, size distortion, and shifts in variance. Our empirical evidence shows that spurious long memory effect in volatility might occur as a result of shifts in variance for some Asian stock markets.

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 Predictability of Scrub Typhus Incidences Time Series in Thailand

2021 ◽  
Vol 5 (1) ◽  
pp. 44
Author(s):  
Valeria Bondarenko ◽  
Pierre Mazzega ◽  
Claire Lajaunie
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Scrub Typhus ◽  
Moving Average ◽  
Public Health Problem ◽  
Reference Level ◽  
Autoregressive Integrated Moving Average ◽  
Incidence Data ◽  
Motion Models ◽  
Moving Average Model

Scrub typhus, an infectious disease caused by a bacterium transmitted by “chigger” mites, constitutes a public health problem in Thailand. Predicting epidemic peaks would allow implementing preventive measures locally. This study analyses the predictability of the time series of incidence of scrub typhus aggregated at the provincial level. After stationarizing the time series, the evaluation of the Hurst exponents indicates the provinces where the epidemiological dynamics present a long memory and are predictable. The predictive performances of ARIMA (autoregressive integrated moving average model), ARFIMA (autoregressive fractionally integrated moving average) and fractional Brownian motion models are evaluated. The results constitute the reference level for the predictability of the incidence data of this zoonosis.

scholarly journals FINANCIAL CONTAGION BETWEEN THE US AND EMERGING MARKETS: COVID-19 PANDEMIC CASE

2020 ◽  
Author(s):  
Paula Heliodoro ◽  
◽  
Rui Dias ◽  
Paulo Alexandre ◽  
◽  
...  
Keyword(s):  
Financial Markets ◽  
Latin American ◽  
Stock Markets ◽  
Null Hypothesis ◽  
Sustainable Growth ◽  
Financial Contagion ◽  
Policy Makers ◽  
Global Pandemic ◽  
The Us ◽  
Bds Test

To realise how crises are disseminated is relevant for policy makers and regulators in order to take appropriate measures to prevent or contain the propagation of crises. This study aims to analysis the financial contagion in the six main markets of Latin America (Argentina, Brazil, Chile, Colombia, Mexico and Peru) and the USA, in the period 2015-2020. Different approaches have been undertaken to carry out this analysis in order to consider the following research question, namely whether: (i) the global pandemic covid19 has accentuated the contagion between Latin American financial markets and the US? The results of the autocorrelation tests are totally coincident with those obtained by the BDS test. The rejection of the null hypothesis, i.i.d., can be explained, among other factors, by the existence of autocorrelation or by the existence of heteroscedasticity in the stock market index series, in which case the rejection of the null hypothesis is explained by non-linear dependence on data, with the exception of the Argentine market. However, significant levels of contagion were expected to occur between these regional markets and the US as a result of the global pandemic (Covid-19), which did not happen. These results may indicate the implementation of efficient diversification strategies. The authors consider that the results achieved are relevance for investors who seek opportunities in these stock markets, as well as for policy makers to carry out institutional reforms in order to increase the efficiency of stock markets and promote the sustainable growth of financial markets.

scholarly journals Spillovers from the Slowdown in China on Financial and Energy Markets: An Application of VAR–VECH–TARCH Models

2020 ◽  
Vol 8 (3) ◽  
pp. 52
Author(s):  
Caner Özdurak ◽  
Veysel Ulusoy
Keyword(s):  
Rare Earth ◽  
Financial Markets ◽  
Stock Markets ◽  
Global Economy ◽  
Global Financial Crisis ◽  
Wide Range ◽  
The Us ◽  
Chinese Stock Markets ◽  
The Impact ◽  
Industrial Metals

The 2008 global financial crisis provides us with a wide range of study fields on cross-asset contagion mechanisms in the US financial markets. After a decade of the so-called subprime crisis, the impact of market news on asset volatilities increased significantly. Consequently, return and volatility spillovers became the most extensive channel for spreading out the news generated in one market to the other ones, which made the financial markets inherit international risk factors as their own local risks. Moreover, as a result of the Chinese economy becoming the main driver of the global economy in the last decade, Chinese markets became more interconnected with developed markets which were followed by a “digital cold war” era via Twitter. In this study, we investigate the relationship between the US stock market, Chinese stock markets, rare earth markets and industrial metals, and mining products via three different models by utilizing VAR–VECH–TARCH models. According to our findings, bilateral spillover exists between US and Chinese stock markets. Cross-market spillovers show that there is a risk transmission channel between the industrial metals, rare earth, and Chinese and US stock markets due to China’s strengthening position in the global economy.

scholarly journals NONSTATIONARITY-EXTENDED WHITTLE ESTIMATION

2009 ◽  
Vol 26 (4) ◽  
pp. 1060-1087 ◽  
Author(s):  
Xiaofeng Shao
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Time Series Models ◽  
Autoregressive Integrated Moving Average ◽  
Asymptotically Normal ◽  
Mild Conditions ◽  
Whittle Estimation ◽  
Moving Average Models ◽  
Memory Time ◽  
Integrated Processes

For long memory time series models with uncorrelated but dependent errors, we establish the asymptotic normality of the Whittle estimator under mild conditions. Our framework includes the widely used fractional autoregressive integrated moving average models with generalized autoregressive conditional heteroskedastic-type innovations. To cover nonstationary fractionally integrated processes, we extend the idea of Abadir, Distaso, and Giraitis (2007, Journal of Econometrics 141, 1353–1384) and develop the nonstationarity-extended Whittle estimation. The resulting estimator is shown to be asymptotically normal and is more efficient than the tapered Whittle estimator. Finally, the results from a small simulation study are presented to corroborate our theoretical findings.

scholarly journals Is There a Causal Relationship Between Financial Markets in Asia and the US?

2017 ◽  
Vol 22 (1) ◽  
pp. 71-90
Author(s):  
Amalendu Bhunia ◽  
Devrim Yaman
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Causal Relationship ◽  
Stock Markets ◽  
Market Prices ◽  
Long Run ◽  
The Us ◽  
Short Run ◽  
International Investors ◽  
Stock Values

This study examines whether there is a causal relationship between selected stock markets in Asia and the US. Based on stock values from a sample of nine Asian stock markets, we find a positive correlation with US stock market prices in most cases, the exception being Vietnam. Our results indicate significant long-run and short-run causality in both directions between the Asian and US stock markets. These findings show that, while both sets of markets are integrated, there are still valuable opportunities for international investors to diversify their portfolios in the US and Asia.

scholarly journals Forecasting Confirmed Cases and Mortalities of COVID-19 in the US

2020 ◽  
Author(s):  
Babak Jamshidi ◽  
Mohsen Kakavandi ◽  
Shahriar Jamshidi Zargaran ◽  
Amir Talaei-Khoei
Keyword(s):  
Time Series ◽  
Reproductive Strategies ◽  
Moving Average ◽  
Large Body ◽  
Cumulative Number ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Moving Average Models ◽  
The Us ◽  
Time Point

AbstractBackgroundThe wide spread of COVID-19 in the US has placed the country as the most infected population worldwide. This paper aims to forecast the number of confirmed cases and mortalities from 12 April to 21 May, 2020. There has been a large body of literature in forecasting epidemic outbreaks such as C algorithms with shortfall of predicting for long periods and autoregressive integrated moving average models with the limited flexibility. However, the US COVID-19 data shows great variety in the relative increments of confirmed cases. This requires a reproductive time series.MethodThis paper suggests a time series based on the relative increments of confirmed cases. The proposed time series assumes the changes in the time series and provides flexibility. The suggested model was applied on the data observed from 27 February to 11 April 2020 and its objective is forecasting 40 days from 12 April to 21 May 2020.ResultsIt is expected that by May 21, 2020, the accumulative number of confirmed cases of COVID-19 in the US rises to 1,464,729, with 80% confidence interval. Our analysis also shows that by the 21st of May, the cumulative number of mortalities caused by COVID-19 in the US from 18747 cases on 11 April increases to around 73250 cases on 21 May, 2020.ConclusionOur results highlight the value of reproductive strategies in time series modelling of COVID-19. Our model benefits from a reproductive strategy from a time point in which the US COVID-19 data demonstrates a sudden fall.

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