Risk Management & Tail Risk

In this paper, we present an approach for estimating significant financial metrics within risk management by utilizing quantum phenomena for random number generation. We explore Quantum-Enhanced Monte Carlo, a method that combines traditional and quantum techniques for enhanced precision through Qua

The inherent volatility and dynamic fluctuations within the financial stock market underscore the necessity for investors to employ a comprehensive and reliable approach that integrates risk management strategies, market trends, and the movement trends of individual securities. By evaluating specifi

We introduce a decision-making framework tailored for the management of systemic risk in networks. This framework is constructed upon three fundamental components: (1) a set of acceptable network configurations, (2) a set of interventions aimed at risk mitigation, and (3) a cost function quantifying

Risk governance is not only about identifying and measuring adverse states of the world. It also asks when an institution is entitled to rely on a risk claim. This paper introduces modal epistemic tools for that second layer of QRM. For a risk proposition $p$, $Kp$ denotes assurance-grade endorsemen

This paper addresses the issue of blockchain protocol risks, a foundational category of risks affecting Distributed Ledger Technology (DLT) which underpins digital assets, smart contracts, and decentralised applications. It presents a comprehensive risk management framework developed in collaboratio

In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identifica

In many real-world datasets, rows may have distinct characteristics and require different modeling approaches for accurate predictions. In this paper, we propose an adaptive modeling approach for row-type dependent predictive analysis(RTDPA). Our framework enables the development of models that can

There is little consensus as to the cause of the housing bubble that precipitated the financial crisis of 2008. Numerous explanations exist: misguided monetary

In this paper, we provide a new property of value at risk (VaR), which is a standard risk measure that is widely used in quantitative financial risk management. We show that the subadditivity of VaR for given loss random variables holds for any confidence level if and only if those are comonotonic.

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropaga

In this paper, we mainly study the impact of the implied certainty equivalent rate on investment in financial markets. First, we derived the mathematical expression of the implied certainty equivalent rate by using put-call parity, and then we selected some company stocks and options; we considered

We provide an elementary proof of the dual representation of Expected Shortfall on the space of integrable random variables over a general probability space. Unlike the results in the extant literature, our proof only exploits basic properties of quantile functions and can thus be easily implemented

This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to comp

This paper delves into the spectrum of credit risks associated with decentralized stablecoin issuance, ranging from overcollateralized lending to business-to-business credit. It examines the mechanisms, risks, and mitigation strategies at each layer, highlighting the potential for scaling decentrali

Artificial intelligence risks are multidimensional in nature, as the same risk scenarios may have legal, operational, and financial risk dimensions. With the emergence of new AI regulations, the state of the art of artificial intelligence risk management seems to be highly immature due to upcoming A

Risk management is a fundamental discipline in project management, which includes, among others, quantitative risk analysis. Throughout several years of teaching, we have observed difficulties in students performing Monte Carlo Simulation within the quantitative analysis of risks. This article aims

Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral

Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks, offering advantages such as enhanced customer experience, democratising financial services, improving consumer protection, and enhancing risk management. However, these complex models

Decision theory recognizes two principal approaches to solving problems under uncertainty: probabilistic models and cognitive heuristics. However, engineers, public planners and decision-makers in other fields seem to employ solution strategies that do not fall into either field, i.e., strategies su

Digital transformation challenges financial management while reducing costs and increasing efficiency for enterprises in various countries. Identifying the transmission paths of enterprise financial risks in the context of digital transformation is an urgent problem to be solved. This paper construc