scholarly journals Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple

2020 ◽  
Vol 13 (6) ◽  
pp. 107
Author(s):  
Pınar Kaya Soylu ◽  
Mustafa Okur ◽  
Özgür Çatıkkaş ◽  
Z. Ayca Altintig
Keyword(s):  
Long Memory ◽  
Value At Risk ◽  
The Other ◽  
Model Method ◽  
Back Testing ◽  
Daily Data ◽  
Rescaled Range ◽  
Auto Regressive ◽  
Student Distribution ◽  
Figarch Model

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.

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.

Statistical benefits of value-at-risk with long memory

2005 ◽  
Vol 7 (4) ◽  
pp. 21-45 ◽  
Author(s):  
Andrea Beltratti ◽  
Claudio Morana
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk

scholarly journals Spatial long memory

2019 ◽  
Vol 3 (1) ◽  
pp. 243-256
Author(s):  
Peter M. Robinson
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Spatial Data ◽  
Long Memory ◽  
The Other ◽  
Future Prospects ◽  
Memory Time ◽  
Short Memory ◽  
The One ◽  
Relevant Work

AbstractWe discuss developments and future prospects for statistical modeling and inference for spatial data that have long memory. While a number of contributons have been made, the literature is relatively small and scattered, compared to the literatures on long memory time series on the one hand, and spatial data with short memory on the other. Thus, over several topics, our discussions frequently begin by surveying relevant work in these areas that might be extended in a long memory spatial setting.

scholarly journals A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets

2019 ◽  
Vol 12 (2) ◽  
pp. 67 ◽  
Author(s):  
Kyriazis
Keyword(s):  
Long Memory ◽  
Detrended Fluctuation Analysis ◽  
Efficient Market Hypothesis ◽  
Trading Strategies ◽  
Fluctuation Analysis ◽  
Efficient Market ◽  
Systematic Survey ◽  
Rescaled Range ◽  
Pricing Behavior ◽  
Detrended Fluctuation

This study conducts a systematic survey on whether the pricing behavior of cryptocurrencies is predictable. Thus, the Efficient Market Hypothesis is rejected and speculation is feasible via trading. We center interest on the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) as well as other relevant methodologies of testing long memory in returns and volatility. It is found that the majority of academic papers provides evidence for inefficiency of Bitcoin and other digital currencies of primary importance. Nevertheless, large steps towards efficiency in cryptocurrencies have been traced during the last years. This can lead to less profitable trading strategies for speculators.

EMPIRICAL LAWS OF A STOCK PRICE INDEX AND A STOCHASTIC MODEL

2003 ◽  
Vol 06 (03) ◽  
pp. 303-312 ◽  
Author(s):  
TAISEI KAIZOJI ◽  
MICHIYO KAIZOJI
Keyword(s):  
Stochastic Model ◽  
Power Law ◽  
Stock Prices ◽  
Stock Price ◽  
Price Index ◽  
Probability Function ◽  
The Other ◽  
Power Law Distribution ◽  
Exponential Distributions ◽  
Daily Data

Recent works by econo-physicists [5,8,15,19] have shown that the probability function of the share returns and the volatility satisfies a power law with an exponent close to 4. On the other hand, we investigated quantitatively the return and the volatility of the daily data of the Nikkei 225 index from 1990 to 2003, and we found that the distributions of the returns and the volatility can be accurately described by the exponential distributions [11]. We then propose a stochastic model of stock markets that can reproduce these empirical laws. In our model the fluctuations of stock prices are caused by interactions among traders. We indicate that the model can reproduce the empirical facts mentioned above. In particular, we show that the interaction strengths among traders are a key variable that can distinguish the emergence of the exponential distribution or the power-law distribution.

Value-at-risk and expected shortfall: a dual long memory framework

2014 ◽  
Vol 16 (4) ◽  
pp. 416
Author(s):  
Zouheir Mighri ◽  
Faysal Mansouri ◽  
Geoffrey J.D. Hewings
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk ◽  
Expected Shortfall

Modeling the risk-return characteristics of the SB1 Mexican private pension fund index

2016 ◽  
Vol 5 (2) ◽  
pp. 70
Author(s):  
Roberto J. Santillán- Salgado ◽  
Marissa Martínez Preece ◽  
Francisco López Herrera
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Financial Time Series ◽  
Private Pension ◽  
Long Term Memory ◽  
Investment Funds ◽  
Risk Return ◽  
Time Series Modelling ◽  
Figarch Model

This paper analyzes the returns and variance behavior of the largest specialized private pension investment funds index in Mexico, the SIEFORE Básica 1 (or, SB1). The analysis was carried out with time series techniques to model the returns and volatility of the SB1, using publicly available historical data for SB1. Like many standard financial time series, the SB1 returns show non-normality, volatility clusters and excess kurtosis. The econometric characteristics of the series were initially modeled using three GARCH family models: GARCH (1,1), TGARCH and IGARCH. However, due to the presence of highly persistent volatility, the series modeling was extended using Fractionally Integrated GARCH (FIGARCH) methods. To that end, an extended specification: an ARFIMA (p,d,q) and a FIGARCH model were incorporated. The evidence obtained suggests the presence of long memory effects both in the returns and the volatility of the SB1. Our analysis’ results have important implications for the risk management of the SB1. Keywords: Private Pension Funds, Time Series modelling, GARCH models, Long Term memory series

The Information Content of Extreme Events Implied in the Non-complete Market

2011 ◽  
Vol 19 (2) ◽  
pp. 121-148
Author(s):  
Moo Sung Kim ◽  
Tae Hun Kang
Keyword(s):  
Extreme Events ◽  
Value At Risk ◽  
Calibration Function ◽  
Market Prices ◽  
Back Testing ◽  
Complete Market ◽  
Testing Performance ◽  
Low Probability ◽  
First Moment ◽  
Option Market

This paper empirically investigates the usefulness of extreme events implied into the non-complete option market in which return generating process of underlying asset is different from that of options. The empirical results find that the information about the extreme events implied in the option market prices has more accurate forecasting power within the tail than near the first moment of realized distribution. So, we expect that the implied information of extreme jump can help to improve the back-testing performance of value at risk where it is primarily important to take account of low-probability events. Regardless of whether calibration function for density transformation is the beta-distribution or non-parametric kernel density, extreme jump provides consistently satisfactory predictions.

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