Evidence Of Long-range Dependence Research Articles

Long-range Dependence in the Carbon Emission Market

This paper investigates the long-range dependence of returns and the volatility of the carbon emission permits in the Chicago Climate Exchange. It is found that contracts with different vintages demonstrate significantly variant long memory properties. In particular, strong long memory is found for vintages from 2003 to 2006, while no evidence of long range dependence in the return series is found for vintage 2007 to vintage 2010.

Measuring price efficiency in petroleum markets: New insights using various long-range dependence techniques

This paper empirically measures pricing efficiency in the petroleum markets using a battery of long-range dependence techniques. We analyse the volatility (absolute returns) of fourteen petroleum products using daily prices from January 1990 to April 2020. We use bootstrapping techniques to estimate the confidence intervals of the long-range dependence parameters. Overall results obtained using CMA-MAF, CMA-MSF, GPH and periodogram techniques suggest that there is no evidence of long-range dependence in absolute returns of petroleum markets prices indices at least at the 10% level of significance. For robustness purposes, findings from the rolling windows estimates reveal that the results are not affected by the normality assumption under different rolling window sizes. Furthermore, we find that Gas oil, Biofuel and Heating oil are the most correctly priced while Crude Oil Dubai emerging is the least efficient. Comparing the efficiency index of the all six crude oil returns in our sample, WTI oil was seen to be less efficient than Brent crude oil. In all, our work uncovers crucial implications for investors and policymakers.

The persistence of economic policy uncertainty: Evidence of long range dependence

In the wake of the recent global financial crisis, Arab spring, serial crises in the Eurozone, and ongoing trade wars among the leading countries in the world, concerns about economic policy uncertainty have heightened. However, there are several aspects of economic policy uncertainty that have not been adequately researched including the persistence of the series. The examination of the persistence of economic policy uncertainty is important. For instance, the size of the uncertainty’ persistence will determine how big the negative effect of an uncertainty shock is on the financial market as well as on the economy. This paper deals with the analysis of economic policy uncertainty for 23 countries, which include developed and developing ones. We focus on persistence by using fractional integration. This is a more general approach than the standard methods based on integer differentiation. The results indicate that the orders of integration are in the interval (0, 1) in all countries, implying long memory and mean reverting behaviour. Thus, shocks will have temporary though long lasting effects. Nevertheless, substantial differences are observed across the countries.

Economic policy uncertainty in G7 countries: evidence of long-range dependence and cointegration

The global financial crisis which emanated from the USA has led to the development of indices for economic policy uncertainty for some developed and developing nations. Also, the current Brexit debate in the UK is a major economic influencer. ‘News of news’ or ‘news of no News’ in the daily newspapers in the USA and UK causes global economic uncertainty which has an aftermath reaction on the global economy. This study considers economic uncertainties in G7 countries using economic policy uncertainty indices developed majorly from newspapers information. The long-range dependence technique in time series was first carried out, and the results reveal an evidence of time series persistence for each country’s index. This provided justification for the adoption of cointegration in a fractional integration set-up using the fractional cointegrating vector autoregressive model recently proposed. The long-run equilibrium results obtained showed that the USA and UK are dominant drivers of economic uncertainty among the G7 countries.

Long-range dependence in the returns and volatility of the Finnish housing market

PurposeThe purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types.Design/methodology/approachFor each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d,q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns.FindingsA higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour.Research limitations/implicationsInvestigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.Originality/valueTo the best of the authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.

Efficiency or speculation? A time-varying analysis of European sovereign debt

The outbreak of the Greek debt crisis caused turmoil in European markets and drew attention to the problem of public debt and its consequences. The increase in the return rates of sovereign debts was one of these consequences. However, like any other asset, sovereign debt returns are expected to have a memoryless behaviour. Analysing a total of 15 European countries (Eurozone and non-Eurozone), and applying a time-varying analysis of the Hurst exponent, we found evidence of long-range memory in sovereign bonds. When analysing the spreads between each bond and the German one, it is possible to conclude that Eurozone countries’ spreads show more evidence of long-range dependence. Considering the Eurozone countries most affected by the Eurozone crisis, that long-range dependence is more evident, but started before the crisis, which could be interpreted as possible speculation by investors.

The EMBI in Latin America: Fractional integration, non-linearities and breaks

This paper analyses the main statistical properties of the Emerging Market Bond Index (EMBI), namely long-range dependence or persistence, non-linearities, and structural breaks, in four Latin American countries (Argentina, Brazil, Mexico, Venezuela). For this purpose it uses a fractional integration framework and both parametric and semi-parametric methods. The evidence based on the former is sensitive to the specification for the error terms, whilst the results from the latter are more conclusive in ruling out mean reversion. Further, non-linearities do not appear to be present. Both recursive and rolling window methods identify a number of breaks. Overall, the evidence of long-range dependence as well as breaks suggests that active policies might be necessary for achieving financial and economic stability in these countries.

Do carry trade returns show signs of long memory?

Abstract Using Hurst coefficient approaches within rescaled range, detrended fluctuation, periodogram regression and average wavelet coefficient frameworks, this article examines the dynamics of currency carry trades. Specifically, we look for evidence of long-range dependence (LRD) in carry trade returns (CTR) of thirteen developed and nine emerging markets. Our analysis provides two important insights. (a) We find strong and robust evidence of LRD for CTR in developed markets and somewhat weaker evidence in emerging markets. Considering that CTR are vital components of modern financial models, these results have far-reaching consequences because, in the presence of LRD, traditional asset pricing methods/tests are no longer valid. (b) We also show that portfolios focusing on only the most persistent carry trades (highest Hurst coefficients among the ones above 0.5) can significantly outperform their all-encompassing counterparts. This implies that Hurst coefficients contain important investment information which can be used to enhance the performance of practical carry trade strategies.

The EMBI in Latin America: Fractional Integration, Non-Linearities and Breaks

This paper analyses the main statistical properties of the Emerging Market Bond Index (EMBI), namely long-range dependence or persistence, non-linearities, and structural breaks, in four Latin American countries (Argentina, Brazil, Mexico, Venezuela). For this purpose it uses a fractional integration framework and both parametric and semi-parametric methods. The evidence based on the former is sensitive to the specification for the error terms, whilst the results from the latter are more conclusive in ruling out mean reversion. Further, non-linearities do not appear to be present. Both recursive and rolling window methods identify a number of breaks. Overall, the evidence of long-range dependence as well as breaks suggests that active policies might be necessary for achieving financial and economic stability in these countries.

The EMBI in Latin America: Fractional Integration, Non-Linearities and Breaks

This paper analyses the main statistical properties of the Emerging Market Bond Index (EMBI), namely long-range dependence or persistence, non-linearities, and structural breaks, in four Latin American countries (Argentina, Brazil, Mexico, Venezuela). For this purpose it uses a fractional integration framework and both parametric and semiparametric methods. The evidence based on the former is sensitive to the specification for the error terms, whilst the results from the latter are more conclusive in ruling out mean reversion. Further, non-linearities do not appear to be present. Both recursive and rolling window methods identify a number of breaks. Overall, the evidence of long-range dependence as well as breaks suggests that active policies might be necessary for achieving financial and economic stability in these countries.

The valuation of equity warrants under the fractional Vasicek process of the short-term interest rate

Motivated by the empirical evidence of long range dependence in short-term interest rates and considering the long maturities of equity warrants, we propose the fractional Vasicek model to describe the dynamics of the short rate in the pricing environment of equity warrants. Using the partial differential equation approach, we present a valuation model for equity warrants under the assumption that the short rate follows the fractional Vasicek process. After identifying the pricing model for equity warrants, we provide the parameter estimation procedure for the proposed pricing model. Since obtaining the values of equity warrants from the proposed model needs to solve a nonlinear equation, we employ a hybrid intelligent algorithm to get around this optimization problem. Furthermore, to illustrate the practicality of our proposed model, we conduct an empirical study to ascertain the performance of our proposed model using the data from China’s warrant market and the China Foreign Exchange Trade System. The comparison of traditional models (such as the Black–Scholes model, the Noreen–Wolfson model, the Lauterbach–Schultz model, and the Ukhov model) with our proposed model is also presented. The empirical results show that the mean absolute percentage error of our pricing model is 10.30%. By contrast, the Black–Scholes model, the Noreen–Wolfson model, the Lauterbach–Schultz model, and the Ukhov model applied to the same warrant produce mean absolute errors of 35.26%, 37.67%, 33.40%, 32.81%, respectively. Thus the long memory property in stochastic interest rates cannot be ignored in determining the valuation of equity warrants.

Torrents on Twitter: Explore Long-Term Social Relationships in Peer-to-Peer Systems

Peer-to-peer file sharing systems, most notably BitTorrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that the long-term relationships among BT peers can be explored to enhance the downloading performance; for example, for re-sharing previously downloaded contents or for effectively collaborating among the peers. However, whether such relationships do exist in real world remains unclear. In this paper, we take a first step towards the real-world applicability of peers' long-term relationship through a measurement based study. We find that 95% peers cannot even meet each other again in the BT networks; therefore, most peers can hardly be organized for further cooperation. This result contradicts to the conventional understanding based on the observed daily arrival pattern in peer-to-peer networks. To better understand this, we revisit the arrival of BT peers as well as their long-range dependence. We find that the peers' arrival patterns are highly diverse; only a limited number of stable peers have clear self-similar and periodic daily arrivals patterns. The arrivals of most peers are, however, quite random with little evidence of long-range dependence. To better utilize these stable peers, we start to explore peers' long-term relationships in specific swarms instead of conventional BT networks. Fortunately, we find that the peers in Twitter-initialized torrents have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Our PlanetLab experiments further indicate that the incorporation of social relations remarkably accelerates the download completion time. The improvement remains noticeable even in a hybrid system with a small set of social friends only.

A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series

Intra-daily financial durations time series typically exhibit evidence of long range dependence. This has motivated the introduction of models able to reproduce this stylized fact, like the Fractionally Integrated Autoregressive Conditional Duration Model. In this work we introduce a novel specification able to capture long range dependence. We propose a three component model that consists of an autoregressive daily random effect, a semiparametric time-of-day effect and an intra-daily dynamic component: the Mixed Autoregressive Conditional Duration (Mixed ACD) Model. The random effect component allows for heterogeneity in mean reversal within a day and captures low frequency dynamics in the duration time series. The joint estimation of the model parameters is carried out using MCMC techniques based on the Bayesian formulation of the model. The empirical application to a set of widely traded US tickers shows that the model is able to capture low frequency dependence in duration time series. We also find that the degree of dependence and dispersion of low frequency dynamics is higher in periods of higher financial distress.

A fractional credit model with long range dependent default rate

Motivated by empirical evidence of long range dependence in macroeconomic variables like interest rates we propose a fractional Brownian motion driven model to describe the dynamics of the short and the default rate in a bond market. Aiming at results analogous to those for affine models we start with a bivariate fractional Vasicek model for short and default rate, which allows for fairly explicit calculations. We calculate the prices of corresponding defaultable zero-coupon bonds by invoking Wick calculus. Applying a Girsanov theorem we derive today’s prices of European calls and compare our results to the classical Brownian model.

Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

Abstract We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA–FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.

Do wheat futures returns exhibit long-range dependence?

The efficient market hypothesis, where asset prices follow a random walk and incorporate all relevant information, is often invoked in financial economics. There is some evidence however to suggest that some asset prices do not follow random walks but display long-range dependence. Such systematic behavior of past returns is of interest to traders. This article examines long-range dependence in wheat futures prices using rescaled range analysis and the Hurst exponent. Since this estimate is biased when long-range dependence is absent and its distribution is unknown, a Monte Carlo simulation approach is proposed. Results show that wheat futures prices show no evidence of long-range dependence and there are no profitable trading rules.

A Bayesian Approach for Capturing Daily Heterogeneity in Intra-Daily Durations Time Series: The Mixed Autoregressive Conditional Duration Model

Intra-daily financial durations time series typically exhibit evidence of long range dependence. This has motivated the introduction of models able to reproduce this stylized fact, like the Fractionally Integrated Autoregressive Conditional Duration Model. In this work we introduce a novel specification able to capture long range dependence. We propose a three component model that consists of an autoregressive daily random effect, a semiparametric time-of-day effect and an intra-daily dynamic component: the Mixed Autoregressive Conditional Duration (Mixed ACD) model. The random effect component allows for heterogeneity in mean reversal within a day and captures low frequency dynamics in the duration time series.The joint estimation of the model parameters is carried out using MCMC techniques based on the Bayesian formulation of the model. The empirical application to a set of widely traded US tickers shows that the model is able to capture low frequency dependence in duration time series. We also find that the degree of dependence and dispersion of low frequency dynamics is higher in periods of higher financial distress.

WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

Fractals or I.I.D.: Evidence of long-range dependence and heavy tailedness from modeling German equity market returns

Several studies find that the return volatility of stocks tends to exhibit long-range dependence, heavy tails, and clustering. Because stochastic processes with self-similarity possess long-range dependence and heavy tails, it has been suggested that self-similar processes be employed to capture these characteristics in return volatility modeling. In this paper, we find using high-frequency data that German stocks do exhibit these stylized facts. Using one of the typical self-similar processes, fractional stable noise, we empirically compare this process with several alternative distributional assumptions in either fractal form or I.I.D. form (i.e., normal distribution, fractional Gaussian noise, generalized extreme value distribution, generalized Pareto distribution, and stable distribution) for modeling German equity market volatility. The empirical results suggest that fractional stable noise dominates these alternative distributional assumptions both in in-sample modeling and out-of-sample forecasting. Our findings suggest that models based on fractional stable noise perform better than models based on the Gaussian random walk, the fractional Gaussian noise, and the non-Gaussian stable random walk.

Sample Size and Accuracy of Estimation of the Fractional Differencing Parameter

Recent empirical studies on human performance in cognitive tasks have provided evidence of long-range dependence in psychological time series. ARFIMA (p, d, q) methodology, an extension of the traditional Box-Jenkins ARIMA modeling, allows estimation of the long-term dependence in the presence of any possible short-memory components. This article examines, by means of Monte Carlo experiments, sample size requirements for the accurate estimation of the long-memory parameter d and documents the quality of the estimates for time series of different length in various (0, d, 0) and (1, d, 1) models. We demonstrate that the conditional sum of squares estimation, a computationally convenient technique available in current versions of SAS for Windows, provides good finite-sample performance, comparable to that of ML. Furthermore, a minimum sample size for parsimonious planning of psychological experiments is recommended.