Liquidity connectedness in cryptocurrency market

We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

Assessing the Evaluability of Adaptation-Focused Interventions: Lessons from the Adaptation Fund

Evaluability assessments (EAs) have differing definitions, focus on various aspects of evaluation, and have been implemented inconsistently in the last several decades. Climate change adaptation (CCA) programming presents particular challenges for evaluation given shifting baselines, variable time horizons, adaptation as a moving target, and uncertainty inherent to climate change and its extreme and varied effects. The Adaptation Fund Technical Evaluation Reference Group (AF-TERG) developed a framework to assess the extent to which the Fund’s portfolio of projects has in place structures, processes, and resources capable of supporting credible and useful monitoring, evaluation, and learning (MEL). The framework was applied on the entire project portfolio to determine the level of evaluability and make recommendations for improvement. This chapter explores the assessment’s findings on designing programs and projects to help minimize the essential challenges in the field. It discusses how the process of EA can help identify opportunities for strengthening both evaluability and a project’s MEL more broadly. A key conclusion was that the strength and quality of a project’s overall approach to MEL is a major determinant of a project’s evaluability. Although the framework was used retroactively, EAs could also be used prospectively as quality assurance tools at the pre-implementation stage.

Approaches to Building Default Probability Models for Financial Instruments of Project Financing at Long Time Horizons

Project financing is one of the priority tools for stimulating the country's economic growth around the world, which allows the implementation of large-scale and capital-intensive projects, providing favorable credit conditions with insufficient creditworthiness of the project beneficiaries [1]. As a rule, project financing instruments are long-term (10-30 years, depending on the type of transaction), so this asset class is interesting for the implementation of the task of building long-term models for assessing credit risk associated with the introduction in 2018 of the new international financial reporting standard IFRS 9 "Financial Instruments". The new standard requires financial institutions to calculate their expected credit loss (ECL) at the time of granting loans and other banking products exposed to credit risk [2], taking into account different time horizons, which significantly changes the traditional approaches to assessing credit risk by commercial banks [3], [4]. As part of this work, a model was built to assess the long-term probability of default for the portfolio of assets of a Russian commercial bank belonging to the project finance segment in accordance with the requirements of the International Financial Reporting standard IFRS 9 "Financial Instruments". At present, the topic of this work is extremely relevant and may be of interest both for commercial banks that are faced with the problem of improving credit risk assessment models

Changing Time Horizons and Trust: Experiences of Aging With Hemophilia

Trust among those who have experienced a lifetime of medical encounters warrants attention to how trust is both cumulative and complex. This study of a historically isolated cohort incorporates interviews (n=25 older adults/professionals) and focus groups uses a lens of trust to highlight the experiences of those aging with hemophilia, individuals who never expected to age. Understood through the lens of trust, the data show evidence of the absence of safe spaces particularly during the early 80s - blood contamination concerns and homophobia-leading often to social withdrawal. Over time, however, some individuals and families created trusted venues to begin demanding research, treatment and policy change. Advocacy re-engaged the community to organize, educate and advance safety protocols for blood product manufacturing and distribution. This presentation will illuminate how experiences with medical providers, contaminated blood supplies, stigma and uncertain in other spheres of one’s life make trust a co-constructed, fragile concept.

Estimating the strength of selection for new SARS-CoV-2 variants

Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present two models for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals that uncertainty is large very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.

COVID-Related Perceptions of the Future and Well-Being Among Older Canadian Women

Socioemotional Selectivity Theory (SST; Carstensen, 1993) posits that time horizons - or Future Time Perspective (FTP) - change with age and/or the priming of endings. Fung and Carstensen (2006) found that SARS-CoV in 2003 naturalistically primed fragility, with consequences for both FTP and well-being. The current SARS-CoV-2 (COVID-19) pandemic provides a similar context: During the early months of COVID-19, age and time horizon were related to greater emotional well-being for American adults (Carstensen et al., 2020); Dozois (2020) found that, for Canadian adults, anxiety and depression rose. The current study examines relationships between FTP, COVID-19 impact, and psychological well-being in older Canadian women (N = 190; Mage = 70.38). We found that COVID-19 impact and FTP were both related to well-being; additionally, COVID-19 impact moderated the relationship between FTP and well-being. The complexity of what remains or becomes increasingly important for older women during a global health crisis is discussed.

A Hydrological Modeling Approach for Assessing the Impacts of Climate Change on Runoff Regimes in Slovakia

The changing climate is a concern with regard to sustainable water resources. Projections of the runoff in future climate conditions are needed for long-term planning of water resources and flood protection. In this study, we evaluate the possible climate change impacts on the runoff regime in eight selected basins located in the whole territory of Slovakia. The projected runoff in the basins studied for the reference period (1981–2010) and three future time horizons (2011–2040, 2041–2070, and 2071–2100) was simulated using the HBV (Hydrologiska Byråns Vattenbalansavdelning) bucket-type model (the TUW (Technische Universität Wien) model). A calibration strategy based on the selection of the most suitable decade in the observation period for the parameterization of the model was applied. The model was first calibrated using observations, and then was driven by the precipitation and air temperatures projected by the KNMI (Koninklijk Nederlands Meteorologisch Instituut) and MPI (Max Planck Institute) regional climate models (RCM) under the A1B emission scenario. The model’s performance metrics and a visual inspection showed that the simulated runoff using downscaled inputs from both RCM models for the reference period represents the simulated hydrological regimes well. An evaluation of the future, which was performed by considering the representative climate change scenarios, indicated that changes in the long-term runoff’s seasonality and extremality could be expected in the future. In the winter months, the runoff should increase, and decrease in the summer months compared to the reference period. The maximum annual daily runoff could be more extreme for the later time horizons (according to the KNMI scenario for 2071–2100). The results from this study could be useful for policymakers and river basin authorities for the optimum planning and management of water resources under a changing climate.

A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities

Nowadays, modeling tools are a crucial part of best practice in the elaboration and implementation of a decarbonization plan in any organization, city, or country. The present review analyzes the different modeling tools available to assess energy systems in smart cities. It creates an updated overview of the modeling tools currently available, showing their capabilities and main potential outputs when considering the energy efficiency objective in the context of smart cities in Europe. A restricted set of 14 tools are identified which optimally fulfill the modeling mission of the energy sector, in a smart city context, for different time horizons. The selection considers the capability to include decarbonization assessments, namely, by considering the flexibility to use different external factors, energy policies, technologies, and mainly the implementation of Article 7 from the Energy Efficiency Directive and the “energy efficiency first” principle defined by the European Commission. The ELECTRE TRI method was used to implement a multi-criteria decision approach for sorting modeling tools, aiming at distributing the various alternatives by previously defined categories, and considering the performance criteria of each alternative modeling tool, the analysis suggests that the best options are the LEAP, MESSAGEix, and oemof tools.