Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange

Anagnostidis, Panagiotis; Emmanouilides, Christos J.

doi:10.1016/j.physa.2014.11.056

Cited by 26 publications

(10 citation statements)

References 66 publications

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“…Indeed, Kumar [28] finds that the long-memory detected by a FIAPARCH model in high frequency USD/INR exchange rate's volatility is stable across time scales; similarly, Anagnostidis and Emmanouilides [29] use an ARMA-FIGARCH model to detect self-similar long-memory in the volatility of Athens composite share price index (ACSPI) returns over several frequencies. Seasonality and long-memory can be filtered out by a flexible Fourier form (FFF) technique and fractionally integrated volatility models [29]. Still, we choose to neglect the effect of volatility in our methodology since it is the distribution of the returns which has a direct effect on volatility dynamics [9].…”

Section: Resultsmentioning

confidence: 99%

Highly flexible distributions to fit multiple frequency financial returns

BenSaïda

Slim

2016

Physica A: Statistical Mechanics and its Applications

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Section: Resultsmentioning

confidence: 99%

Highly flexible distributions to fit multiple frequency financial returns

BenSaïda

Slim

2016

Physica A: Statistical Mechanics and its Applications

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“…For the better understanding of the empirical part, in Table 1 six major crises that occurred throughout the whole time-period of observations are reported. 1 [Insert Table somewhere here]…”

Section: Methodsmentioning

confidence: 99%

Time series analysis of S&P 500 index: A horizontal visibility graph approach

Vamvakaris

Pantelous

Zuev

2018

Physica A: Statistical Mechanics and its Applications

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The behavior of stock prices has been studied extensively throughout the last century, and contradictory results have been reported in the corresponding literature. In this paper, we provide another perspective using a network theoretical approach to investigate how crises affected the behavior of US stock prices. We analyze high frequency data from S&P500 via the Horizontal Visibility Graph method, and find that all major crises that took place in the last twenty years, worldwide, affected significantly the behavior of the price-index. Nevertheless, we observe that each of those crises impacted the index in a different way or with a different magnitude. Interestingly, our results suggest that the predictability of the price-index series increases during the periods of crises.

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“…The incomprehensiveness of literature in the area of persistence lies in the fact that the major focus of these studies is equity markets, and very few focus on other types of security viz. bonds, FX or DR markets (Malkei, 2003;Beine & Laurent, 2003;Oh, Kim & Eom, 2006;Kumar and Maheswaran, 2013;Madhavan, 2014;Anagnostidis and Emmanouilides, 2015;Ferreira & Dionisio, 2016;Sensoy & Tabak, 2016;and Masa and Diaz, 2017).…”

Section: Persistence In Financial Asset Marketsmentioning

confidence: 99%

Long Term Dynamics of Indian ADRs Market: The Case of Persistence and Irregular Cycles

Bora¹,

Kumar

2017

AFR

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The focus of this study is to understand the previously ignored return generating dynamics of American Depositary Receipts (ADR) markets. The main objective of this study is to investigate the nature of the return generating process of the Indian ADRs market. Specifically, the study addresses following interrelated research questions: Do returns series of Indian ADRs market exhibit random walk behavior or rather depict persistence and nonlinear dynamics? Is there any cyclicity in the returns series of Indian ADRs market? Rescaled Range (R/S) method on daily and weekly return series of Bank of the New York Mellon Indian ADR index (BKIN) from 2002 to 2016 has been applied to address the above questions. Empirical findings revealed that returns series of Indian ADRs market: (a) do not exhibit random walk behavior and rather depict both nonlinear behavior and persistence (long range dependence); (b) possess non-periodic cycles of 0.793, 2.38 and approximately 7 years. The findings can work as crucial inputs to forecasting, risk-management and market regulation processes. The knowledge of the average cycle length and persistence will enhance preparedness to handle the opportunities and risks at all levels in the market.

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Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange

Cited by 26 publications

References 66 publications

Highly flexible distributions to fit multiple frequency financial returns

Highly flexible distributions to fit multiple frequency financial returns

Time series analysis of S&P 500 index: A horizontal visibility graph approach

Long Term Dynamics of Indian ADRs Market: The Case of Persistence and Irregular Cycles

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