We investigate the impact of the Basel Fundamental Review of the Trading Book (FRTB) on banks’ market risk capital requirements under the internal models approach. To do this, we take a stylized portfolio sensitive to the risk factors affected by the FRTB, representative of a typical trading book. Our assessment spans the period 2007–19. We find that the FRTB will entail sizable increases in the regulatory capital intended to absorb market shocks. These increases originate not only from the change in the risk measure and taking longer liquidity horizons (the latter having a greater impact on portfolios more focused on bonds) but also from the strict limitation of portfolio diversification benefits. Our study should be of interest to bank supervisors and regulators, risk managers and other decision makers within the banking industry.

This paper introduces quantile models that incorporate realized variance, realized semivariance, jump variation and jump semivariation based on a conditional autoregressive quantile regression model framework for improved value-at-risk (VaR) and improved joint forecasts of VaR and expected shortfall (ES), which we denote by .VaR; ES/. Our empirical results show that high-frequency-data-based realized quantities lead to better VaR and .VaR; ES/ forecasts. We evaluate these using conditional coverage and dynamic quantile backtests for VaR, regression-based backtests for .VaR; ES/ and comparison tests based on scoring functions and model confidence sets. The study includes data sets covering the global financial crisis of 2007–9 and the Covid-19 pandemic to ensure stability over different market conditions. The results indicate that realized quantity extensions improve forecasts in terms of classic and comparison tests for all quantile levels and time periods, with stand-alone VaR forecasts benefiting the most. It is shown that the symmetric absolute value quantile model benefits the most from realized semivariance extension, whereas the asymmetric slope model benefits the most from realized variance extension.

With climate change, physical and transition climate risks are increasing and affecting the credit risk characteristics of individual obligors and portfolios of credit obligations, such as credit cards, mortgages and loans. To accommodate the effects of physical climate risks, we first estimate the frequency of extreme weather events for different US states by using state-specific meteorological data. Using these estimates, the probability of default is calculated for an obligor in a particular state. For a pair of obligors, a closed-form expression (up to a summation) is derived for the probability of default of two companies. A recent Bank of England conference addressed the importance of scenario analysis. This paper shows how to incorporate the effects of physical and transition risks using a multiperiod scenario analysis. This facilitates the estimation of different risk measures. Physical and transition risks can significantly increase the probability of default, value-at-risk and expected shortfall. The magnitude of these changes depends on the nature of the different risk parameters and the initial creditworthiness of a company.

We study combinations of risk measures under no restrictive assumption on the set of alternatives. We develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures. One of the main results is the representation for resulting risk measures from the properties of both alternative functionals and combination functions. To that, we build on the development of a representation for arbitrary mixture of convex risk measures. In this case, we obtain a penalty that recalls the notion of inf-convolution under theoretical measure integration. As an application, we address the context of probability-based risk measurements for functionals on the set of distribution functions. We develop results related to this specific context. We also explore features of individual interest generated by our framework, such as the preservation of continuity properties, the representation of worst-case risk measures, stochastic dominance and elicitability. We also address model uncertainty measurement under our framework and propose a new class of measures for this task.

We consider time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components. For this purpose we adopt established allocation principles. We also use our approach to obtain forecasts for changes in the risk of the portfolio’s components. To put the approach into practice we present an implementation based on the output of a simulation. Allocation is illustrated with an example portfolio in the context of Solvency II. The quality of the forecasts is investigated with an empirical study.

This study examines the impacts of financial and macroeconomic factors on financial stability in emerging countries by focusing on Turkey's banking sector. In this context, financial stability is represented by nonperforming loans (NPLs). Four financial and three macroeconomic indicators as well as the Covid-19 pandemic are included as explanatory variables. Quarterly data from 2005 Q1 to 2020 Q3 are analyzed by using the residual augmented least squares unit root test and generalized method-ofmoments. The empirical results show the following: credit volume, which is a financial indicator, has the greatest effect on NPLs; risk-weighted assets, unemployment rate, foreign exchange rate and economic growth all have a statistically significant impact on NPLs; the Covid-19 pandemic has had an increasing impact on NPLs; inflation and interest rates have a positive coefficient, as expected, although they are not statistically significant. These results highlight the importance of financial factors (ie, credit volume and risk-weighted assets) over macroeconomic factors in terms of NPLs. Based on the empirical results of the study, we suggest Turkish policy makers focus primarily on financial variables (ie, credit growth and risk-weighted assets) as well as considering the effects of other factors.

Target-date funds are mutual funds with a date in their description. The date signifies the retirement date for the person for whom the mutual fund is designed. The basic proposition is that the fund’s composition will be adjusted as it evolves toward that date, which will relieve the investor of the problem of asset allocation. These funds received a significant boost in 2006 when they became the default choice of many defined contribution plans. However, in our 2011 paper in The Journal of Risk we showed that many funds significantly increased their allocation toward equities immediately prior to the 2007–9 global financial crisis and consequently saw significant losses. Since this was only shortly after target-date funds became a significant component of the mutual fund market, this research assesses the maturation of target-date funds and their performance during the Covid-19 pandemic, when there were again significant market losses. Overall, our assessment is that target-date funds have largely met their designation and there is no evidence of them similarly gaming their asset allocation as occurred prior to the financial crisis.

Insurance funds have attracted increasing attention as a large amount of long-term funds has been invested in the banking industry. Using the stock return data of Chinese-listed banks, this paper measures the systemic risk contagion effect of banks via the least absolute shrinkage and selection operator–vector autoregression (LASSO–VAR) high-dimensional method and generalized variance decomposition. It also investigates the threshold effect of insurance institutional shareholding on the systemic risk contagion of banks. The results show that shareholding by insurance institutions can curb the contagion of banking systemic risks. However, as the shareholding ratio increases, this inhibitory effect weakens. In the heterogeneity analysis, securities investment funds, trust funds and the Qualified Foreign Institutional Investor (QFII) program did not play a role in reducing systemic risk contagion, and traditional insurance and dividend insurance were the main insurance funds stabilizing the market. Further research finds that banks’ operating decisions and operating risks act as internal transmission channels for insurance institutional shareholding to reduce their risk spillover effect. This paper expands the research on the factors influencing bank systemic risk, which is of great significance for preventing and resolving bank systemic risk and promoting the sustainable and healthy development of the banking system.

Attempts to allocate capital across a selection of different investments are often hampered by the fact that investors' decisions are made under limited information (no historical return data) and during an extremely limited timeframe. Nevertheless, in some cases, rational investors with a certain level of experience are able to ordinally rank investment alternatives through relative assessments of the probabilities that investments will be successful. However, to apply traditional portfolio optimization models, analysts must use historical (or simulated/expected) return data as the basis for their calculations. This paper develops an alternative portfolio optimization framework that is able to handle this kind of information (given by an ordinal ranking of investment alternatives) and to calculate an optimal capital allocation based on a Cobb-Douglas function, which we call the Sorted Weighted Portfolio (SWP). Considering risk-neutral investors, we show that the results of this portfolio optimization model usually outperform the output generated by the (intuitive) Equally Weighted Portfolio (EWP) of different investment alternatives, which is the result of optimization when one is unable to incorporate additional data (the ordinal ranking of the alternatives). To further extend this work, we show that our model can also address risk-averse investors to capture correlation effects.

While risk factor disclosures in 10-K filings have been criticized by practitioners as generic and boilerplate, recent studies indicate that these risk reports can be informative. This study contributes to the ongoing discussion by investigating whether risk factor disclosures contain valuable information that can be used to improve the estimation of the covariance matrix of stock returns. In particular, we examine the 10-K and 10-Q filings of firms listed in the Standard & Poor’s 100 index from 2006 to 2020. We compute cosine similarity measures to compare risk factor reports and use them in linear regressions to estimate the covariance matrix of stock returns. Our estimators using risk report data outperform well-established sample-based estimators, such as the shrinkage estimator of Ledoit and Wolf. This indicates that risk factor disclosures are informative and contain information that is not already reflected in historical stock prices. This information can be used to improve portfolio selection and thus generate economic value.