The crypto-asset ecosystem is still in its infancy, and it currently faces a number of threats and opportunities ahead. In a recent speech from the Governor of the Bank of England (Carney, 2018) , it reads "The time has come to hold the crypto-asset ecosystem to the same standards as the rest of the financial system."
Bringing such ecosystem to the same standards entails in particular understanding and quantifying the risks associated with those assets.
The methods introduced in this paper will showcase how the comovement of extreme losses of these assets evolve over time-and how abruptly the dynamics governing such assets can change. The recent manuscript by Gkillas and Katsiampa (2018) offers a first step in this direction, yet: i) their analysis is for the univariate case-whereas here we model comovements of extreme losses; ii) their focus is on the stationary setting-and thus cannot account for the dynamics of bivariate extreme values over time. Again confined to the univariate case only, Ardia et al. (2018) provides evidence of volatility regime changes for the log-returns of the Bitcoin crypto-currency.
Studies of the contemporaneous occurence of extreme events for crypto-currencies have only recently started to appear. By considering the pairwise dependence of ten of the largest cryprocurrencies, concluded that overall crypto-assets exhibit strong levels of tail dependence. However, their analysis is for a stationary setting only as in Gkillas and Katsiampa (2018) .
Statistical models for nonstationary multivariate extremes have only very recently been devised (de Carvalho, 2016; Mhalla et al., 2017; Escobar-Bach et al., 2018; Castro et al., 2018; Gong and Huser, 2019; Mhalla et al., 2019) . In particular, Gong and Huser (2019) investigated the dynamics of the extreme dependence between the crypto-currencies Bitcoin and Ethereum and noticed that it has been significantly becoming stronger over the years, thus highlighting the need for a non-stationary analysis. Yet all methods previously proposed assume that the dependence between the multivariate extremes evolves smoothly over time,
whereas in contexts such as in Economics and Finance it may be more sensible to allow for structural breaks and change-points.
The approach proposed in this paper is the first attempt to model such breaks and change-points in the extremal dependence structure using extreme value theory. To achieve this, we extend the approach of Hanson et al. (2017) by allowing the change-points to be incorporated in the extremal dependence structure. Our approach paves the way for learning about breaks in the dynamics governing joint extremes over time. Specifically, we model the angular surface of a time-varying extreme value copula by resorting to Bernstein polynomials, and allow for the inclusion of change-points in the parameters of the copula. A different approach is proposed by Dias and Embrechts (2013) consisting of a testing procedure to identify structural changes in the dependence structure of a copula function.
We define the time-varying bivariate extreme value (BEV) distribution as
for t ∈ T ⊆ R, and x, y > 0. Here {H t } is a family of probability measures satisfying
The interpretation of h t is as follows: the more mass h t puts around 1/2 the higher the dependence between the extremes; whereas the more mass h t puts around 0 and 1 the higher the independence. Similarly, the time-varying extreme value copula can be defined
Our main object of interest is the conditional angular density h t which is modelled in a flexible but parametric fashion and is henceforth denoted h t,θ . We take a change-point approach to model the non-stationarity of the angular density. With a slight abuse of
where θ = (θ 1 , θ 2 , τ ) is the parameter vector. The model therefore consists of two regimes divided at an unknown point τ such that within each regime the model is a stationary, parametric angular density h θ i , for i = 1, 2.
Each angular density in (2) is modelled using Bernstein polynomials as in Hanson et al. (2017) , which are reviewed next. A Bernstein polynomial of order J ∈ N and parameter θ is
is the Dirichlet density, α 1 +α 2 =J θ α 1 ,α 2 = 1 and θ = {θ α 1 ,α 2 : α 1 + α 2 = J and α 1 , α 2 ∈ N}.
To ensure the constraint in (1) is respected the following equality must hold
The Bernstein polynomial angular density is defined as the Bernstein polynomial in (3) obeying (4). The change-point angular density model is defined as in (2) where h θ i are Bernstein polynomial angular densities.
A Bayesian approach is taken here for estimation and therefore the model definition is completed by an appropriate prior distribution. Here we choose to give independent priors so that
where π(θ i ), i = 1, 2, is Dirichlet as in Hanson et al. (2017) and π(τ ) is uniform in T .
Given a sample w 1 , . . . , w T , the posterior distribution is proportional to
Estimation of θ is carried out using a componentwise adaptive Markov chain Monte Carlo (MCMC) algorithm. The estimation of the parameter θ i , i = 1, 2, follows the algorithm of Hanson et al. (2017) where in this case we use the adaptive step of Roberts and Rosenthal (2009) . The change-point is estimated via a Metropolis-Hastings step where the proposal distribution is a truncated normal (to be between 0 and T ) with mean centred at the current value of the algorithm and variance adjusted adaptively as in Roberts and Rosenthal (2009) . For each pair of crypto-assets this procedure returns a sample (e 1,1 , e 1,2 ), . . . , (e N,1 , e N,2 ) of residuals on the same scale. The so-called pseudo-angles and radii are respectively constructed as w i = e i,1 e i,1 + e i,2 , r i = e i,1 + e i,2 , i = 1, . . . , N.
A sample from the conditional angular density h t can then be obtained by retaining only those pseudo-angles w i for which the radius r i exceeds a large threshold. Here the threshold is selected as the 90% percentile of (r 1 , . . . , r N ) and the final sample consists of 186 pseudoangle exceedances for each pair of assets. 41B USD, respectively, these are used to illustrate our results.
The change-point angular density model in (2) In all cases the first regime is characterized by very weak extreme dependence with extreme events occurring independently between assets. Conversely, the second regime exhibits a stronger extreme co-movements between any pair of assets.
This is illustrated in Figure 2 for Bitcoin/Ethereum. For the first regime (centre plot) most of the mass of the pseudo-angles is concentrated around 0 and 1, indicating that extreme events happened independently for the two crypto-currencies. Conversely, for the 
We have proposed a novel approach based on the formal framework of extreme value theory to identify abrupt changes in the extreme comovements of crypto-currencies as well as model their dependence structure. Our results suggest that in the past three years big joint losses between crypto-currencies have became more likely, with a regime change often happening around the beginning of the 2018 crypto-currencies crisis. Therefore, our results overall agree with those of Feng et al. (2018) and Gong and Huser (2019) , but here we provide a model-based estimate of when such a regime change might have happened.
Although we focused in this paper on a single change-point and on the bivariate setting, extreme value theory provides the foundations to extend our approach to the multivariate case, thus providing a unique picture of the dependence between crypto-assets. Furthermore, there is no conceptual problem in generalizing our approach for an unknown number of change-points which can be estimated from data, though inference would become more challenging. The above-mentioned extensions are the topic of ongoing research.