A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading...

It was evident that credit default swap (CDS) spreads have been highly correlated during the recent financial crisis. Motivated by this evidence, this study attempts to investigate the extent to which CDS markets across regions, maturities and credit ratings have integrated more in crisis. By...

A standard quantitative method to access credit risk employs a factor model based on joint multi- variate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate...

Abstract: We account for time-varying parameters in the conditional expectile based value at risk (EVaR) model. EVaR appears more sensitive to the magnitude of portfolio losses compared to the quantile-based Value at Risk (QVaR), nevertheless, by fitting the models over relatively long ad-hoc...

Classical asset allocation methods have assumed that the distribution of asset returns is smooth, well behaved with stable statistical moments over time. The distribution is assumed to have constant moments with e.g., Gaussian distribution that can be conveniently parameterised by the first two...

Systemically important banks are connected and have dynamic dependencies of their default probabilities. An extraction of default factors from cross-sectional credit default swaps (CDS) curves allows to analyze the shape and the dynamics of the default probabilities. Extending the Dynamic Nelson...

In this paper we propose a new bootstrap, or Monte-Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages,...

The big bang of non-fungible tokens (NFTs) has caused the birth of a brand new era for digital art. NFTs, driven by blockchain and smart contracts, provide both artists and art collectors an unprece- dented marketplace equipped with more security, flexibility, publicity, and freedom to monetize....

Deribit exchange offers about 90\% open interest in the recent cryptocurrency options market. The dominating type of options listed in Deribit is the inverse BTC option, which is settled in BTC and thus allows professional traders to avoid frequent convert between cryptocurrency and fiat...

Bitcoin Pricing Kernels (PK) are estimated using a novel data set from Deribit, one of the largest Bitcoin derivatives exchanges. The PKs improve the understanding of investor sentiment and risk premia. Bootstrap-based confidence bands are estimated in order to validate the results. Investors...