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This Special Issue covers the topic of timely vital risk management - systemic risk - from many important perspectives. It includes novel and scientific approaches from the network with topological indicators on systemic risk, community analysis of the global financial system, welfare analysis of capital insurance and the impact of capital requirement, risk measures, and optimal portfolio and optimal reinsurance under risk constraint. Most articles study the financial sector and insurance companies after the financial crisis of 2008–2009 circa ten years prior. The COVID-19 global pandemic in 2020 has caused similar or even greater challenges for the entire economy. Therefore, this Special Issue will be useful for anyone interested in systemic risk management.

Coins, banknotes, medals, seals (numismatics) --- optimal reinsurance --- general risk measure --- risk sharing --- systemic risk --- capital insurance --- welfare --- equilibrium --- conditional value-at-risk --- mean-CVaR portfolio optimization --- risk minimization --- Neyman–Pearson problem --- interconnectedness --- financial conglomerate --- contagion --- capital requirement for premium risk --- collective risk model --- reinsurance strategies --- Solvency II --- community structure --- complex networks --- financial markets --- insurance sector --- deltaCoVaR --- minimum spanning trees—topological indicators --- tail dependence

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The existence of financial intermediaries is arguably an artifact of information asymmetry. Beyond simple financial transactions, financial intermediation provides a mechanism for information transmission, which can reduce the degree of information asymmetry and consequently increase market efficiency. During the process of information transmission, the bank is able to provide unique services in the production and exchange of information. Therefore, banks have comparative advantages in information production, transmission, and utilisation. This book provides an overview of commercial banking and includes empirical methods in banking such risk and bank performance, capital regulation, bank competition and foreign bank entry, bank regulation on bank performance, and capital adequacy and deposit insurance.

Coins, banknotes, medals, seals (numismatics) --- deposit insurance --- capital adequacy --- bank risk --- foreign bank entry --- bank competition --- H-statistics --- pooled regression --- dynamic panel models --- risk-taking behavior --- banks --- efficiency --- data envelopment analysis --- Asia-Pacific --- regulations --- bank capital --- meta-analysis --- Bayesian model-averaging --- capital regulation --- competition --- Indian banking sector --- panel data --- revenue diversification --- bank risks --- bank performance --- net interest income --- non-interest income --- risks --- capital

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Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored.

Coins, banknotes, medals, seals (numismatics) --- recovery rates --- beta regression --- credit risk --- contingent convertible debt --- financial modelling --- risk management --- financial crisis --- recovery rate --- loss given default --- model ambiguity --- default time --- no-arbitrage --- reduced-form HJM models --- recovery process --- Counterparty Credit Risk --- Hidden Markov Model --- Risk Factor Evolution --- Backtesting --- FX rate --- Geometric Brownian Motion --- trade credit --- small and micro-enterprises --- financial non-financial variables --- risk assessment --- logistic regression --- probability of default --- wrong-way risk --- dependence --- urn model --- counterparty risk --- credit valuation adjustment (CVA) --- XVA (X-valuation adjustments) compression --- genetic algorithm --- n/a

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Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Coins, banknotes, medals, seals (numismatics) --- Index parameter --- estimation --- wrapped stable --- Hill estimator --- characteristic function-based estimator --- asymptotic --- efficiency --- GARCH model --- HARCH model --- PHARCH model --- Griddy-Gibs --- Euro-Dollar --- safe-haven assets --- gold price --- Swiss Franc exchange rate --- oil price --- generalized Birnbaum–Saunders distributions --- ACD models --- Box-Cox transformation --- high-frequency financial data --- goodness-of-fit --- banking competition --- credit risk --- NPLs --- Theil index --- convergence analysis --- interest rates --- yeld curve --- no-arbitrage --- bonds --- B-splines --- time series --- multifractal processes --- fractal scaling --- heavy tails --- long range dependence --- financial models --- Bitcoin --- capital asset pricing model --- estimation of systematic risk --- tests of mean-variance efficiency --- t-distribution --- generalized method of moments --- multifactor asset pricing model --- Lerner index --- stochastic frontiers --- shrinkage estimator --- seemingly unrelated regression model --- multicollinearity --- ridge regression --- financial incentives --- public service motivation --- job performance --- job satisfaction --- intention to leave

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Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.

Coins, banknotes, medals, seals (numismatics) --- cluster analysis --- equity index networks --- machine learning --- copulas --- dependence structures --- quotient of random variables --- density functions --- distribution functions --- multi-factor model --- risk factors --- OLS and ridge regression model --- python --- chi-square test --- quantile --- VaR --- quadrangle --- CVaR --- conditional value-at-risk --- expected shortfall --- ES --- superquantile --- deviation --- risk --- error --- regret --- minimization --- CVaR estimation --- regression --- linear regression --- linear programming --- portfolio safeguard --- PSG --- equity option pricing --- factor models --- stochastic volatility --- jumps --- mathematics --- probability --- statistics --- finance --- applications --- investment home bias (IHB) --- bivariate first-degree stochastic dominance (BFSD) --- keeping up with the Joneses (KUJ) --- correlation loving (CL) --- return spillover --- volatility spillover --- optimal weights --- hedge ratios --- US financial crisis --- Chinese stock market crash --- stock price prediction --- auto-regressive integrated moving average --- artificial neural network --- stochastic process-geometric Brownian motion --- financial models --- firm performance --- causality tests --- leverage --- long-term debt --- capital structure --- shock spillover