Are ETFs, digital assets and credit default swap connected? A fresh insight into dynamic modelling

As cyber threats continue to evolve, organizations are increasingly adopting advanced security frameworks such as Zero Trust Architecture (ZTA) to protect their digital assets. ZTA operates under the principle of "never trust, always verify," acknowledging that both internal and external networks may be compromised. Integrated with Identity and Access Management (IAM), ZTA offers a dynamic security model, requiring continuous authentication and authorization for every access request. This integration ensures that sensitive resources are protected by enforcing multifactor authentication, behavioral analysis, and micro-segmentation, which collectively reduce the risk of attacks. A critical aspect of ZTA is its focus on continuous authentication and the principle of least privilege. Unlike traditional IAM systems, which grant static access, ZTA dynamically adjusts access based on real-time conditions, such as user behavior, location, and the sensitivity of data. This reduces the attack surface and minimizes lateral movement within the network. Additionally, ZTA promotes network segmentation, using IAM frameworks to enforce strict access control for each network segment. This limits the potential damage if an attacker compromises one segment. Studies have shown that ZTA is highly effective in mitigating insider threats, reducing the attack surface, and enhancing the resilience of infrastructure. While challenges remain in terms of scalability and integration complexity, the security benefits are undeniable. The increasing use of AI, machine learning, and quantum computing is expected to further strengthen ZTA's security capabilities. As organizations face more sophisticated cyber threats, ZTA is likely to become a standard in cybersecurity strategies, providing a robust, adaptable solution to protect valuable digital assets.

Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors

Estimating a Covariance Matrix for Market Risk Management and the Case of Credit Default Swaps

Credit risk modeling is about modeling losses. These losses are typically coming unexpectedly and triggered by shocks. So any process modeling the stochastic nature of losses should reasonable include jumps. In this paper we review a few jump driven models for the valuation of Credit Default Swaps (CDSs) and show how under these dynamic models also pricing of (exotic) derivatives on single name CDSs is possible. More precisely, we set up fundamental firm-value models that allow for fast pricing of the ‘vanillas’ of the CDS derivative markets: payer and receiver swaptions. It turns out that the proposed model is able to produce realistic implied volatility smiles. Moreover, we detail how a CDS spread simulator can be set up under this framework and illustrate its use for the pricing of exotic derivatives with single name CDSs as underliers.

This study examines the policy determinants of bank default risk, proxied by credit default swap (CDS) spreads, using quarterly data from the U.S. and Canada for the period 2020 to 2022. The analysis considers four determinant groups: the COVID-19 pandemic shock, aggressive monetary and fiscal policy responses, macroprudential-related bank financial fundamentals, and macroeconomic and market conditions. Utilizing General Least Squares (GLS) and dynamic panel models with system General Method of Moments (GMM), the findings reveal that rising COVID-19 deaths significantly widen CDS spreads. Expansionary monetary policies reduce bank default risk, though the effectiveness under conventional monetary policy is reduced by increasing COVID-19 deaths. In contrast, expansionary fiscal policies and inflation broaden CDS spreads. The combined analysis highlights policy stances as dominant factors, with macroprudential-related fundamentals having minimal impact. The findings are robust using both 5-year and 10-year CDS measures. This study has significant policy implications during health and economic crises. For financial stability, policymakers should prioritize reducing pandemic-related deaths, utilize expansionary monetary policies with health support strategies, balance fiscal support to avoid excessive government spending, and implement measures to control inflation. It is essential to enhance coordination between healthcare, fiscal, and monetary authorities for effective policy implementation.

This study aims to investigate the existence of contagion between liquid and illiquid assets in the credit default swap (CDS) market around the recent financial crisis. The authors perform analyses based on vector autoregression model and the dynamic conditional correlation model. The estimation of vector autoregression models reveals that changes in liquid CDS (LCDS) spreads lead to changes in illiquid CDS spreads at least one week ahead during the financial crisis period, whereas the leading direction is reversed during the post-crisis period. Moreover, the results are robust after controlling for structural variables which are proven as determinants of CDS spreads and are empirically supported. This study interprets that information was incorporated first into the LCDSs because of the flight-to-liquidity during the recent crisis period but there is a default contagion effect by reflecting illiquidity-induced credit risk after the crisis. Finally, the dynamic conditional correlation analysis also confirms the main results.

Data Science looks at raw numbers and informational objects created by different disciplines. The Digital Society creates information and numbers from many scientiHic disciplines. The amassment of data though makes is hard to Hind structures and requires a skill full analysis of this massive raw material. The thoughts presented here on DS2 - Data Science & Digital Society analyze these challenges and offers ways to handle the questions arising in this evolving context. We propose three levels of analysis and lay out how one can react to the challenges that come about. Concrete examples concern Credit default swaps, Dynamic Topic modeling, Crypto currencies and above all the quantitative analysis of real data in a DS2 context.

Objective: to analyze the possibilities of overcoming the problems of the Russian economy related to sanctions through the introduction of a two-circuit national monetary and financial system using digital financial assets; to assess the effectiveness of such a system to stimulate economic growth, reduce inflation and ensure financial stability under the sanctions pressure.Methods: the analysis uses methods of mathematical modeling of the Russian economy dynamics, taking into account the interaction of the financial and manufacturing sectors. A system of differential equations is used to describe the cash flows in the economy, as well as scenarios with different levels of investment and CFA issuance.Results: mathematical modeling was used to analyze the prospects for Russia’s economic development under the Western sanctions policy. It showed that, although the emergency anti-sanctions measures taken by the Russian government since the beginning of the special military operation have proven effective, in the long term a significant change in monetary and financial policy is necessary, as the existing rules and methods of its implementation do not contribute to solving the problems that have arisen. The article uses mathematical modeling to study the effect of introducing a two-circuit monetary and financial system using digital financial assets. The calculations have shown that the use of a two-circuit system together with an active investment policy significantly increases the Russian economy efficiency (even under Western sanctions), saturating it with money for GDP growth and at the same time reducing the inflationary pressure of a growing money supply.Scientific novelty: the authors propose a new financial system that combines digital assets and dual goods to ensure the economy sustainability under the sanctions. The authors also developed a mathematical model that makes it possible to assess the long-term consequences of the two-circuit national monetary and financial system and compare it with traditional financing methods.Practical significance: the results of the analysis and mathematical modeling can be used in the implementation of the monetary and financial policy in the Russian Federation under the sanctions. The introduction of the two-circuit system will make it possible to target financial resources to the manufacturing sector, reduce inflation and ensure the ruble exchange rate stability.

Valuing portfolio credit derivatives, such as CDO tranches based on a broad portfolio of credits, like the CDX.NA.IG index portfolio with 125 investment grade names, remains a very difficult problem. Only approximate and imperfect solutions are available so far. The industry standard Gaussian copula model has several known shortcomings, including the fact that it is a static model which does not offer a clear way to tie together valuation for CDO tranches with different maturities, and it doesn9t generate wide enough spreads to match market prices for the supersenior tranches. Efforts to make the default hazard rate dynamic by modeling it as a diffusion run into the second difficulty when the models are calibrated to market prices. This article introduces a dynamic process for the cumulative hazard rate for a credit portfolio, that allows discrete jumps with jump size increasing in the number of jumps. This approach ties together the market quotes for CDO tranches of different maturities into a unified valuation framework. It also can generate large enough default intensity once several defaults have occurred to produce default risk on the senior and supersenior tranches of the magnitude that the market seems to be incorporating into their prices. <b>TOPICS:</b>Credit default swaps, factor-based models, CLOs, CDOs, and other structured credit

Subprime residential mortgage securitization and its associated risks have been a major topic of discussion since the onset of the mortgage crisis in July 2007. In this paper, we provide a stochastic dynamic model for investing bank profit under mortgage securitization. In addition, aspects of this model are illustrated by means of a numerical example. In addition, we solve a stochastic optimal credit default insurance problem that has the cash outflow rate for satisfying depositor obligations, the investment in structured mortgage products and credit default insurance as controls. As far as the latter is concerned, we compute credit default swap and accrued premiums by considering the credit rating of structured mortgage products such as residential mortgage-backed securities and collateralized debt obligations. Copyright © 2011 John Wiley & Sons, Ltd.