Recent empirical studies have demonstrated long-memory in the signs of orders to buy or sell in financial markets. We show how this can be caused by delays in market clearing. Under the common practice of order splitting, large orders are broken up into pieces and executed incrementally. If the size of such large orders is power law distributed, this gives rise to power law decaying autocorrelations in the signs of executed orders. More specifically, we show that if the cumulative distribution of large orders of volume v is proportional to v{-alpha} and the size of executed orders is constant, the autocorrelation of order signs as a function of the lag tau is asymptotically proportional to tau{-(alpha - 1)}. This is a long-memory process when alpha lt; 2. With a few caveats, this gives a good match to the data. A version of the model also shows long-memory fluctuations in order execution rates, which may be relevant for explaining the long-memory of price diffusion rates.

Many complex systems present an intrinsic bipartite nature and are described and modeled in terms of networks. Bipartite networks are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set and the heterogeneity makes it very difficult to discriminate preferential links between the elements from randomly occurring links reflecting system heterogeneity. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis, which takes into account system heterogeneity. We apply our method to a biological, an economic and a social complex system. Our method is able to detect network structures which are informative about the organization and specialization of the investigated systems. Specifically, our method (i) identifies the preferential relationships between the elements, (ii) highlights the clustered structure of systems, and (iii) defines and classifies links according to the type of statistically validated relationships between the connected nodes.

This paper provides empirical evidences that corporate firms risk assessment could benefit from taking quantitatively into account the network of interactions among firms. Indeed, the structure of interactions between firms is critical to identify risk concentration and the possible pathways of propagation of financial distress. In this work, we consider the interactions by investigating a large proprietary dataset of payments among Italian firms. We first characterise the topological properties of the payment networks, and then we focus our attention on the relation between the network and the risk of firms. Our main finding is to document the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to predict the missing rating of a firm using only network properties of a node by means of machine learning methods.

We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequently, we find that the discretized process is also long memory with the same Hurst exponent as the original process. We consider the properties of two estimators of the Hurst exponent, namely the local Whittle (LW) estimator and the Detrended Fluctuation Analysis (DFA). By using analytical considerations and numerical simulations we show that, in presence of round-off error, both estimators are severely negatively biased in finite samples. Under regularity conditions we prove that the LW estimator applied to discretized processes is consistent and asymptotically normal. Moreover, we compute the asymptotic properties of the DFA for a generic (i.e., non-Gaussian) long memory process and we apply the result to discretized processes.

Negative emissions technologies (NETs) feature prominently in most scenarios that halt climate change and deliver on the Paris Agreement's temperature goal. As of today, however, their maturity and desirability are highly debated. Since the social value of new technologies depends on how novel knowledge fuels practical solutions, we take an innovation network perspective to quantify the multidimensional nature of knowledge spillovers generated by twenty years of research in NETs. In particular, we evaluate the likelihood that scientific advances across eight NET domains stimulate (i) further production of knowledge, (ii) technological innovation, and (iii) policy discussion. Taking as counterfactual scientific advances not related to NETs, we show that NETs-related research generates overall significant, positive knowledge spillovers within science and from science to technology and policy. At the same time, stark differences exist across carbon removal solutions. For example, the ability to turn scientific advances in NETs into technological developments is a nearly exclusively feature of Direct Air Capture (DAC), while Bio-energy with Carbon Capture and Storage (BECCS) lags behind. Conversely, BECCS and Blue Carbon (BC) have gained relative momentum in the policy and public debate, vis-Ã -vis limited spillovers from advances in DAC to policy. Moreover, both scientific advances and collaborations cluster geographically by type of NET, which might affect large-scale diffusion. Finally, our results suggest the existence of coordination gaps between NET-related science, technology, and policy.

Repurchase agreements (repos) are one of the most important sources of funding liquidity for many financial investors and intermediaries. In a repo, some assets are given by a borrower as collateral in exchange of funding. The capital given to the borrower is the market value of the collateral, reduced by an amount termed haircut (or margin). The haircut protects the the capital lender from loss of value of the collateral contingent on the borrower's default. For this reason, the haircut is typically calculated with a simple Value at Risk estimation of the collateral for the purpose of preventing the risk associated to volatility. However, other risk factors should be included in the haircut and a severe undervaluation of them could result in a significant loss of value of the collateral if the borrower's defaults. In this paper we present a stylized model of the financial system, which allows us to compute the haircut incorporating the liquidity risk of the collateral and, most important, possible systemic effects. These are mainly due to the similarity of bank portfolios, excessive leverage of financial institutions, and assets illiquidity. The model is analytically solvable under some simplifying assumptions and robust to the relaxation of these assumptions, as shown through Monte Carlo simulations. We also show which are the most critical model parameters for the determination of haircuts.