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Riot Points (RP) in League of Legends game content or for aesthetic customization of your character with matching skins. Or you can buy more champions, or boosts. Riot Points cannot be used directly to influence the game.
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Want to buy League of Legend skins or boosts to give your character that winning edge? Use this digital gift card to easily purchase your Riot Points fast. Riot Points, or RP, is the in-game currency used in the wildly popular online game, League of Legends. The game, released in 2009 by Riot Games, is largely based on an adaptation of Warcraft lll and is a multiplayer online battle arena game. Teams challenge one another to see who comes out victorious. Each team of 5 is made up of Spellcasters, Marksmen, Supports, Assassins and more. You choose your champion, so customizing your character is the best way to enhance your adventure. Buy Riot Points with a League of Legends Gift Card and exchange them for premium in-game content like skins to help your champion stand out. With new skins, your champion gains new visual effects, animations or even voice-overs! Aside from cosmetic options, RP can also be used for additional rune pages, server/name changes, level boosts, mystery chests and more. Your RP credit never expires, so use it when you want!
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Such analyses about the abilities of Bitcoin led to a more recent strand in the literature that aims to compare Bitcoin with gold properties [13] - [18] . They found commonalities and differences between Bitcoin and gold, developed well in [16] especially. These commonalities lead [19] to call the Bitcoin the digital gold. Besides, both commodities have a debatable intrinsic value that some authors discuss specifically [2] [17] [18] [20] . While gold is primarily used for its store of value abilities, his safe haven abilities are often considered in the literature. Bitcoin abilities remain even more uncertain.
Our paper aims to assess the predictive power of gold prices for Bitcoin prices and brings two main contributions to the previous literature. Firstly, it brings a methodological contribution since we use a threshold regression to take into consideration potential structural breaks, after testing for the structural stability of the parameters based on recursive residuals and the cumulative sum of square (CUSUM) statistic. The paper appears therefore to complement the recent wave of studies using mainly GARCH models [16] [20] or OLS and quantile approaches [21] . [21] for instance found a significant but weak negative causality that they explain by the fact that as a choice of an investment, risk-averse investors may prefer gold, while investors with speculative motive may prefer Bitcoin. Secondly, we use the largest time span in the literature, from July 19. 2010 to December 31. 2018 which allows us to take into account recent variations in the Bitcoin prices that were not included in previous studies.
The rest of the paper is organized as follows. Section 2 provides a data description. In Section 3, we present the methodology, while in section 4 we discuss empirical findings in light with the recent literature. Section 5 concludes.
The evolution in the gold price is volatile over the entire period but to a less extent. We distinguish a turning point in 2013. Before this turning point, the regime is characterized by a high mean, while after that we note a downward slope associated with a low mean. From pictorial analysis, it is reasonable to expect 1) the presence of a deterministic trend for both series on one hand and 2) one or more structural breaks on the other.
Most of the macroeconomics temporal variables could inhibit structural breaks and an ignorance to the same could results into misleading results [23] . This study uses the threshold regression model employed by [24] and [25] to investigate the relation between Bitcoin and gold prices using time as the threshold variable. Such model is essentially concerned with modeling non-linearity. Besides, this model is robust to unknown forms of heteroscedasticity, something that cannot be said of the traditional Chow test [26] .
For robustness check, we run the Chow first test [25] for parameter stability, commonly used in time series analysis despite its drawback in detecting unknown forms of heteroscedasticity. The test allows determining whether a single linear regression is more efficient than two separate regressions involving splitting the data into two sub-samples, from the break point. The test consists in estimating three different regressions, one over the whole period and then two for each sub-period. We obtain three sums of squared residuals that are used to run an F-test, with the parameter stability being null hypothesis. We compute log differences for all the series to approximate their respective growth rate.
Going deeper into the analysis, we plot the recursive cumulative sum in Figure 2 and run a unit root test with structural break to statistically confirm the presence of one or more breaks in the relation, and to identify the period of this (these) potential break(s).
Figure 2 shows the results of the recursive cumulative sum test plot which includes a 95% confidence band around the null value. It indicates that the curve structurally drifts outside the confidence band in the end of the plot, from the middle of 2017 to December 2018, which suggests that there are structural breaks in the relationship seemingly occurring around October-November 2017. The shape of the curve advocates for a hinge threshold, with a close-to-zero slope before the break.
A unit root test with structural break is run to determine the date of the break (Table 4). The Wald statistics has a p-value lower than 1% indicating that we can reject the null hypothesis of no structural break and accept the alternative one. There is a structural break which occurs on the 5th of October 2017. We saw from Figure A1 that such break is nourished by an impressive growing trend in Bitcoin prices and a quite stable evolution in gold prices. Overall, the structural
Table 5 reports the result of the Chow test for parameter stability. We find evidence of a structural break on October 5. 2017 with a p-value equals to 0.0017. Therefore we confidently reject the null hypothesis that parameters are stable over time at the 1% significance level and validate our threshold regression.
This study uses cross-sectional data to investigate the Bitcoin-gold prices nexus over the daily period 2010 and 2018. This study contrasts with others in the previous literature, because it uses both linear and nonlinear threshold regression models and tests the structural stability of the parameters based on recursive residuals. 041b061a72