Why the long-term auto-correlation has not been eliminated by arbitragers: Evidences from NYMEX

Li, Daye; Nishimura, Yusaku; Men, Ming

doi:10.1016/j.eneco.2016.08.006

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…We find statistically significantly higher Sharpe ratios for most specifications of our active trading strategy (see Harris and Nguyen, 2013, for the suggestion to evaluate portfolios based on ARFIMA forecasts with the help of Sharpe ratios). This result is in line with Li, Nishimura and Men (2016) who find that, in the majority of cases, a trading strategy based on long-memory forecasts of NYMEX futures, and implemented with the help of binary options, will produce higher Sharpe ratios than those of a moving average or momentum strategy. 6 We interpret this result as strong support for the notion that volatility forecasts convey material information about Chinese companies in the REE industry, and are therefore of strong economic importance.…”

Section: Accepted Manuscriptsupporting

confidence: 89%

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The economic importance of rare earth elements volatility forecasts

Proelss

Schweizer

Seiler

2020

International Review of Financial Analysis

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We compare the suitability of short-memory models (ARMA), long-memory models (ARFIMA), and a GARCH model to describe the volatility of rare earth elements (REEs). We find strong support for the existence of long-memory effects. A simple long-memory ARFIMA() baseline model shows generally superior accuracy both in-and out-of-sample, and is robust for various subsamples and estimation windows. Volatility forecasts produced by the baseline model also convey material forward-looking information for companies in the REEs industry. Thus, an active trading strategy based on REE volatility forecasts for these companies significantly outperforms a passive buy-and-hold strategy on both an absolute and a risk-adjusted return basis.

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Section: Accepted Manuscriptsupporting

confidence: 89%

“…-Please insert Figure 1 about hereand avoid any influence from currency fluctuations, we use Chinese companies only, so that both the companies and the REEs are traded in renminbi. 6 However, the trading strategy of Li, Nishimura and Men (2016) was not profitable after accounting for trading costs.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The economic importance of rare earth elements volatility forecasts

Proelss

Schweizer

Seiler

2020

International Review of Financial Analysis

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“…And Wang and Liu (2010) found that crude oil markets are moving towards market efficiency in time but the trend is not stable. After 2010, there are many more examples of a successful utilization of methods from outside of economics and finance on economic, financial, and specifically also energy problems (for the recent ones, see Kristoufek and Vosvrda (2014), Sensoy and Hacihasanoglu (2014), Kristoufek and Lunackova (2015), Lubnau and Todorova (2015), Gu and Zhang (2016) and Li et al (2016)).…”

Section: Introductionmentioning

confidence: 99%

Are the crude oil markets really becoming more efficient over time? Some new evidence

Krištoufek

2019

Energy Economics

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We replicate the study of Tabak & Cajueiro (2007): "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility" published in Energy Economics 29, pp. 28-36. The results have been mostly confirmed. Specifically, we have confirmed that the crude oil markets efficiency could be rejected up till approximately 1994 and this holds for both studied crude oil commodities -Brent and WTI. Analyzing an extended dataset up till June 2017, we find that the markets remained efficient (at least with respect to long-range dependence) until the outbreak of the Global Financial Crisis in 2008. This is confirmed by all three applied methods -the rescaled range analysis used in the original study, and the detrended fluctuation analysis and the Geweke-Porter-Hudak estimator which were added for stronger validity of the results. The markets returned back to efficiency around 2012 and remained there until 2015 when the Hurst exponent started another rally and stayed rather high until the end of the examined sample. Comparing the two markets, the Brent crude oil shows stronger signs of inefficiency during the inefficient periods compared to the WTI crude oil. This is in hand with the results reported in the original study. Apart from rerunning the analysis on an extended dataset and using two additional methods, we also provide a firmer validity check using the movingblock bootstrap procedure, which outperforms the original shuffling procedure in the provided forecasting study.

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“…In recent decades, the introduction of time scaling techniques in the characterization of long-range correlation of time series have attracted considerable attention and turned into a busy field of research, compared to short-range methods such as the Markov process [32]- [34] and auto regressive models [35], [36]. In long range correlations (persistence), the lag-1 detrended ACF(C (1)) and the scaling exponent called Hurst (h) are the basic variables [37]- [39].…”

Section: Introductionmentioning

confidence: 99%

Long Range Correlation in Vegetation Over West Africa From 1982 to 2011

et al. 2019

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Satellite-derived Normalized Difference Vegetation index (NDVI) data records offer important sources for long term correlation modelling over West Africa. In this study, we assessed long range correlations in half monthly NDVI records over West Africa from 1982 to 2011 using GIMMS NDVI. In our analysis, we assessed (a) the annual and seasonal trends obtained using Ordinary Linear Regression, (b) the detrended lag-1-autocorrelation C(1), (c) the Detrended Fluctuation Analysis (DFA) scaling Hurst exponent h and (d) the Multifractal (MF) characteristics of NDVI. Results show that there exist some patterns or trends in the records that persist over time. The value of C(1) for NDVI was obtained as 0.989 is significant at 95% confidence interval. Consequently, the scaling h values of the Hurst DFA showed that about 37.4, 20.5, 41.7 and 0.5% of the vegetated areas are anti-correlated (h < 0.5), un-correlated (h = 0.5), correlated (0.5 < h < 1) and uncorrelated random walk (h = 1), respectively. The trend analysis from Ordinary Least square Regression (OLR) shows that about 54.3, 0.1 and 45.6% of the vegetated areas are positively, uncorrelated and negatively correlated, respectively. Our findings revealed that the DFA method performed better than OLR and the findings could be useful in identifying areas with improved and degraded vegetation, which cannot be properly captured by the OLR method. Accordingly, the comparison of the MF-DFA results of original data to those of shuffled and surrogate series indicated that the multifractal nature of considered time-series is both from PDF and long-range correlations but arguably, MF due to long range correlation dominates over West Africa. The research is therefore helpful in the formulating crop and environmental management policies that may be used to improve ecosystem management using a long term plan (inter-annual) or short term (inter-seasonal) planning. INDEX TERMS Long range correlation, multifractality, NOAA AVHRR, NDVI, ecological zones, West Africa.

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Why the long-term auto-correlation has not been eliminated by arbitragers: Evidences from NYMEX

Cited by 8 publications

References 41 publications

The economic importance of rare earth elements volatility forecasts

The economic importance of rare earth elements volatility forecasts

Are the crude oil markets really becoming more efficient over time? Some new evidence

Long Range Correlation in Vegetation Over West Africa From 1982 to 2011

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