Adaptive market hypothesis: An empirical analysis of the Wine Market

We test the nature of weak form informational efficiency present in the wine market using daily return of LIV-EX 50 index from 1/1/2010 to 12/6/2020. First, we employ a number of statistical tests including variance ratio tests, tests for linear and non-linear dependence and Hurst coefficient. The tests are applied on the full dataset and on four non overlapping sub-samples of equal length. The variance ratio tests provide a mixed regarding informational efficiency. Evidence of non-linear dependence in the return series was found. The Hurst coefficient values confirm the presence of long run persistence in the wine market. Based on the mixed evidence, we test the possibility of adaptive nature of the wine market. We employ the newly proposed Adaptive Index (AI) to quantify the degree of information inefficiency in the wine market at any instance. Our results confirm that wine market is adaptive and periodically shifts between states of efficiency and inefficiency. The wine market is found to be relatively free from the Covid-19 induced shock and the safe haven property of wine is thus confirmed. Finally, impact of various macroeconomic and financial events on wine market efficiency is identified by using AI.

Calculating Hurst Exponent with the Use of the Siroky Method in Developed and Emerging Markets

The purpose of the article This paper analysis Hurst exponents calculated with the use of the Siroky method in two time intervals of 625 (H625) and 1250 (H1260) sessions for the following assets: (the number of assets for a given group in brackets): Stock indices (74), currency pairs divided into segments: USD exchange rate in relation to 42 other currencies (USDXXX), EURO exchange rate in relation to 41 other currencies (EURXXX), JPY exchange rate in relation to 40 other currencies (JPYXXX) and other currency pairs (12). In total, 209 financial instruments were analyzed. Methodology: Hurst coefficient calculation with the use of the following methods; Siroky, Detrended Moving Average (DMA) and Detrended Fluctuation Analysis (DFA). Results of the research: The Hurst coefficient values calculated with the use of Siroky method are similar to the results obtained using DFA and DMA methods. The second main conclusion that was drawn from the research may be formulated as follows: exchange rates calculated for the developed-developed country currencies are more effective than in the case of the developed-emerging countries group.

Detection of the botnets’ low-rate DDoS attacks based on self-similarity

An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.

On Estimating the Hurst Parameter from Least-Squares Residuals. Case Study: Correlated Terrestrial Laser Scanner Range Noise

Many signals appear fractal and have self-similarity over a large range of their power spectral densities. They can be described by so-called Hermite processes, among which the first order one is called fractional Brownian motion (fBm), and has a wide range of applications. The fractional Gaussian noise (fGn) series is the successive differences between elements of a fBm series; they are stationary and completely characterized by two parameters: the variance, and the Hurst coefficient (H). From physical considerations, the fGn could be used to model the noise of observations coming from sensors working with, e.g., phase differences: due to the high recording rate, temporal correlations are expected to have long range dependency (LRD), decaying hyperbolically rather than exponentially. For the rigorous testing of deformations detected with terrestrial laser scanners (TLS), the correct determination of the correlation structure of the observations is mandatory. In this study, we show that the residuals from surface approximations with regression B-splines from simulated TLS data allow the estimation of the Hurst parameter of a known correlated input noise. We derive a simple procedure to filter the residuals in the presence of additional white noise or low frequencies. Our methodology can be applied to any kind of residuals, where the presence of additional noise and/or biases due to short samples or inaccurate functional modeling make the estimation of the Hurst coefficient with usual methods, such as maximum likelihood estimators, imprecise. We demonstrate the feasibility of our proposal with real observations from a white plate scanned by a TLS.

APPLICATION OF HURST INDICATOR TO CHOOSE AN ALGORITHM FOR RESOURCE CONTROL OF A TELECOMMUNICATION NETWORK

It has been shown that fuzzy integrals (Sugeno and Shocke) have special properties and are suitable for a fuzzy system for managing the resources of a telecommunication network. The form of choosing a method for calculating the Hurst coefficient in a fuzzy control system for telecommunication network resources is proposed.

PRICE–VOLUME DEPENDENCE OF BITCOIN AND ITS FRACTAL ANALYSIS

Nowadays Bitcoin as cryptocurrency takes a significant place on the global financial markets. This paper analyzes the Bitcoin closing prices and traded volume during the period from December 28, 2013 to January 22, 2019. This period is known as a period with rapid increasing of the Bitcoin closing prices, mainly in the second half of the year 2017. The aim of this paper is twofold. First, we compute the Hurst coefficient to discover the close price dynamics and traded volume using a fractal point of view. We have discovered an anti-persistent behavior in the traded volume and random character of bitcoin closing prices. Second, we propose an analysis of the relationship between the close prices and traded volume. Our findings show how changes in the high-price period differ from changes in the low-price period. We also found that high prices caused investors to be afraid to trade due to possible rapid decrease in bitcoin closing prices.