scholarly journals Testing for long memory in volatility in the Indian Forex market

2014 ◽  
Vol 59 (203) ◽  
pp. 75-90 ◽  
Author(s):  
Anoop Kumar
Keyword(s):  
Long Memory ◽  
Foreign Exchange Market ◽  
Memory Models ◽  
Conditional Variance ◽  
Forecast Performance ◽  
Conditional Mean ◽  
The Us ◽  
Short Memory ◽  
Two Measures

This article attempts to verify the presence of long memory in volatility in the Indian foreign exchange market using daily bilateral returns of the Indian Rupee against the US dollar from 17/02/1994 to 08/11/2013. In the first part of the analysis the presence of long-term dependence is confirmed in the return series as well as in two measures of unconditional volatility (absolute returns and squared returns) by employing three measures of long memory. Next, the presence of long memory in conditional volatility is tested using ARMA-FIGARCH and ARMA-FIAPARCH models under various distributional assumptions. The results confirm the presence of long memory in conditional variance for two models. In the last part, the presence of long memory in conditional mean and conditional variance is verified using ARFIMA-FIGARCH and ARFIMA-FIAPARCH models. It is also found that long-memory models fare well compared to short-memory models in sample forecast performance.

Response Asymmetries in Asian Stock Markets

2005 ◽  
Vol 08 (04) ◽  
pp. 637-657 ◽  
Author(s):  
Shuh-Chyi Doong ◽  
Sheng-Yung Yang ◽  
Thomas C. Chiang
Keyword(s):  
Stock Returns ◽  
Conditional Variance ◽  
Autocorrelation Coefficient ◽  
Conditional Mean ◽  
Asymmetric Effects ◽  
The Past ◽  
The Us ◽  
Mean And Variance ◽  
Conditional Variance Models ◽  
The Impact

This paper examines autocorrelation and cross-autocorrelation patterns for selected Asian stock returns. Special attention is given to examination of Asian stock returns and the impact on them of the past information. By employing a class of asymmetric specification of conditional mean and conditional variance models, we find the autocorrelation coefficient to be negative for the Japanese market and positive for the rest of the Asian markets studied. Our findings suggest that the Asian markets respond sensitively to the US market, especially on the down side. The asymmetric effects are found to be present in both mean and variance equations. The evidence is consistent with behavior in which investors in Asian markets tend to react more significantly to negative stock news originating from US sources than they do to positive news.

scholarly journals THE RISK PREMIUM IN THE FOREIGN EXCHANGE MARKET. THE APPLICATION OF ARCH-IN-MEAN MODEL

2020 ◽  
Vol 21 (2) ◽  
pp. 71-79
Author(s):  
Katarzyna Czech
Keyword(s):  
Foreign Exchange ◽  
Risk Premium ◽  
Foreign Exchange Market ◽  
Conditional Variance ◽  
Exchange Market ◽  
Market Uncertainty ◽  
Forward Premium Anomaly ◽  
The Us ◽  
High Volatility ◽  
Japanese Yen

Forward premium anomaly is one of the most popular puzzles in the theory of international finance. The phenomenon is explained by, among others, the existence of non-zero risk premium in the foreign exchange market. The paper applies ARCH-in-mean models to assess whether there exists a time-varying risk premium in the USD/PLN and AUD/JPY foreign exchange markets. The results indicate the existence of a non-zero risk premium in the analyzed markets. As far as the USD/PLN is concerned, the risk premium takes negative values when the risk measured by conditional variance rises. The results suggest that when there is a surge in risk, the US dollar’s appreciation and Polish zloty depreciation increases. The results confirm the US dollar as a safe-haven currency that tends to appreciate during high-volatility and crisis periods. Moreover, the study shows that the risk premium in the AUD/JPY market takes positive values when the risk measured by conditional variance rises. It implies that when there is a mount in risk, the appreciation of Japanese yen increases. Furthermore, research results reveal the positive and significant relationship between stock market uncertainty and exchange rates conditional volatility.

scholarly journals Evaluation of Long Memory on the Malaysia Exchange Rate Market

2018 ◽  
pp. 653-656
Author(s):  
Atikullah Ibrahim ◽  
Siti Aida Sheikh Hussin ◽  
Zalina Zahid ◽  
SitiShalizaMohd Khairi
Keyword(s):  
Exchange Rate ◽  
Long Memory ◽  
Arima Model ◽  
Likelihood Estimation ◽  
Model Forecast ◽  
Monthly Data ◽  
The Us ◽  
Us Dollar ◽  
Arfima Model

This research evaluates the presence of long memory or long-term dependence on the Malaysian exchange rate. Daily, weekly and monthly data are evaluated against the US dollar (USD) covering from January 2005 to March 2018. Evaluation of long memory is based on the Geweke and Porter-Hudak estimation and the Maximum Likelihood Estimation. The result suggests the presence of long memory on all the daily, weekly and monthly data. Results show that shock on the Malaysian exchange rate persist longer than expected. The forecast capability also concludes that addition of the long memory presence from ARIMA model to ARFIMA model could improve the model forecast.

Long Memory Models

2018 ◽  
Author(s):  
Peter Robinson
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Data ◽  
Financial Time Series ◽  
Moving Average ◽  
Memory Models ◽  
Series Data ◽  
Short Memory ◽  
Macroeconomic Time Series ◽  
Mean Variance

Long memory models are statistical models that describe strong correlation or dependence across time series data. This kind of phenomenon is often referred to as “long memory” or “long-range dependence.” It refers to persisting correlation between distant observations in a time series. For scalar time series observed at equal intervals of time that are covariance stationary, so that the mean, variance, and autocovariances (between observations separated by a lag j) do not vary over time, it typically implies that the autocovariances decay so slowly, as j increases, as not to be absolutely summable. However, it can also refer to certain nonstationary time series, including ones with an autoregressive unit root, that exhibit even stronger correlation at long lags. Evidence of long memory has often been been found in economic and financial time series, where the noted extension to possible nonstationarity can cover many macroeconomic time series, as well as in such fields as astronomy, agriculture, geophysics, and chemistry. As long memory is now a technically well developed topic, formal definitions are needed. But by way of partial motivation, long memory models can be thought of as complementary to the very well known and widely applied stationary and invertible autoregressive and moving average (ARMA) models, whose autocovariances are not only summable but decay exponentially fast as a function of lag j. Such models are often referred to as “short memory” models, becuse there is negligible correlation across distant time intervals. These models are often combined with the most basic long memory ones, however, because together they offer the ability to describe both short and long memory feartures in many time series.

scholarly journals Long Memory Process in Asset Returns with Multivariate GARCH Innovations

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Imène Mootamri
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
Asset Returns ◽  
Multivariate Garch ◽  
Time Varying ◽  
Memory Process ◽  
Conditional Moment ◽  
Conditional Mean ◽  
Long Memory Process

The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long-term dependence in stock returns. More precisely, the long-term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process, and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confirm the presence of long memory property in the conditional mean of all stock returns.

scholarly journals Modeling and Forecasting the Volatility of Gas Futures Prices

2018 ◽  
Vol 15 (4) ◽  
pp. 511
Author(s):  
Fernando Antonio Lucena Aiube ◽  
Carlos Patrício Samanez ◽  
Larissa De Oliveira Resende ◽  
Tara Keshar Nanda Baidya
Keyword(s):  
New York ◽  
Long Memory ◽  
Conditional Variance ◽  
Futures Contracts ◽  
Modeling And Forecasting ◽  
Out Of Sample ◽  
Long Memory Processes ◽  
Short And Long Term ◽  
Class Models

We examine the ability of three different GARCH-class models, with four innovation distributions, to capture the volatility properties of natural gas futures contracts traded on the New York Mercantile Exchange. We jointly estimate the long-memory processes for conditional return and variance investigating the long-memory and persistence of long and short maturities contracts. We examine the ability of these models and distributions to forecast the conditional variance. We find that AR(FI)MA-FIAPARCH model is a better fit for short- and long-term contracts. However, there is not a single innovation distribution that provides a better fit for all of the data examined. The out-of- sample forecast of variance also provides mixed results concerning the best innovation distribution. Further, the persistence decreases as the maturity of contracts increases.

Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility

2018 ◽  
Vol 21 (02) ◽  
pp. 1850008 ◽  
Author(s):  
Geoffrey Ngene ◽  
Ann Nduati Mungai ◽  
Allen K. Lynch
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Parameter Estimates ◽  
Dependency Structure ◽  
Fractional Differencing ◽  
Short Memory ◽  
The Impact ◽  
Sector Returns ◽  
Daily Returns

The study investigates the impact of structural breaks on the long memory of daily returns and variance of 11 sectors. Using multiple sequential structural breaks tests, we uncover numerous and roughly shared structural breaks. Results from two non-parametric, three semi-parametric, and three parametric fractional differencing models using break-adjusted and break-unadjusted returns reveal incidence of short memory and anti-persistence in sector returns. Regarding variance, we find that the removal of breaks from the sector series dampens the fractional differencing parameter estimates. Therefore, the observed long memory in variance may be attributable to the occurrence of structural breaks in the sector series.

scholarly journals Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH

2015 ◽  
Vol 13 (3) ◽  
pp. 394
Author(s):  
Alex Sandro Monteiro De Moraes ◽  
Antonio Carlos Figueiredo Pinto ◽  
Marcelo Cabus Klotzle
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Memory Models ◽  
Garch Models ◽  
Daily Data ◽  
Short Memory ◽  
Forecasting Volatility

This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock indices. We used daily data from 1999 to 2014 and an adaptation of the Monte Carlo simulation to estimate VaR and ES forecasts for multiple steps ahead (1, 10 and 20 days ), using FIGARCH and GARCH models for four errors distributions. The results suggest that, in general, the FIGARCH models improve the accuracy of forecasts for longer horizons; that the error distribution used may influence the decision about the best model; and that only for FIGARCH models the occurrence of underestimation of the true VaR is less frequent with increasing time horizon. However, the results suggest that rolling sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models.

Moving past the vaccine/autism controversy - to examine potential vaccine neurological harms

2020 ◽  
pp. 1-15
Author(s):  
Peter R. Breggin
Keyword(s):  
Neurological Disorders ◽  
Developmental Disorder ◽  
Physical Symptoms ◽  
Psychosocial Disorders ◽  
The Us ◽  
Potential Vaccine ◽  
Research And Education ◽  
The Brain ◽  
New Vaccines

BACKGROUND: The vaccine/autism controversy has caused vast scientific and public confusion, and it has set back research and education into genuine vaccine-induced neurological disorders. The great strawman of autism has been so emphasized by the vaccine industry that it, and it alone, often appears in authoritative discussions of adverse effects of the MMR and other vaccines. By dismissing the chimerical vaccine/autism controversy, vaccine defenders often dismiss all genuinely neurological aftereffects of the MMR (measles, mumps, and rubella) and other vaccines, including well-documented events, such as relatively rare cases of encephalopathy and encephalitis. OBJECTIVE: This report explains that autism is not a physical or neurological disorder. It is not caused by injury or disease of the brain. It is a developmental disorder that has no physical origins and no physical symptoms. It is extremely unlikely that vaccines are causing autism; but it is extremely likely that they are causing more neurological damage than currently appreciated, some of it resulting in psychosocial disabilities that can be confused with autism and other psychosocial disorders. This confusion between a developmental, psychosocial disorder and a physical neurological disease has played into the hands of interest groups who want to deny that vaccines have any neurological and associated neuropsychiatric effects. METHODS: A review of the scientific literature, textbooks, and related media commentary is integrated with basic clinical knowledge. RESULTS: This report shows how scientific sources have used the vaccine/autism controversy to avoid dealing with genuine neurological risks associated with vaccines and summarizes evidence that vaccines, including the MMR, can cause serious neurological disorders. Manufacturers have been allowed by the US Food and Drug Administration (FDA) to gain vaccine approval without placebo-controlled clinical trials. CONCLUSIONS: The misleading vaccine autism controversy must be set aside in favor of examining actual neurological harms associated with vaccines, including building on existing research that has been ignored. Manufacturers of vaccines must be required to conduct placebo-controlled clinical studies for existing vaccines and for government approval of new vaccines. Many probable or confirmed neurological adverse events occur within a few days or weeks after immunization and could be detected if the trials were sufficiently large. Contrary to current opinion, large, long-term placebo-controlled trials of existing and new vaccines would be relatively easy and safe to conduct.

Sign in / Sign up

Export Citation Format

Share Document