Showing papers in "Journal of Risk in 2023"
TL;DR: In this paper , the authors examined the impacts of financial and macroeconomic factors on financial stability in emerging countries by focusing on Turkey's banking sector, which is represented by nonperforming loans (NPLs).
Abstract: 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.
1 citations
TL;DR: Wang et al. as discussed by the authors investigated the threshold effect of insurance institutional shareholding on the systemic risk contagion of banks and found that shareholding by insurance institutions can curb the contagions of banking systemic risks.
Abstract: 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.
TL;DR: In this article , the authors investigated whether risk factor disclosures contain valuable information that can be used to improve the estimation of the covariance matrix of stock returns and found that risk factor disclosure is informative and contain information that is not already reflected in historical stock prices.
Abstract: 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.
TL;DR: In this article , the authors examined the high-frequency intraday return and volatility transmission between crude oil futures prices and exchange rates during the 2020 Covid-19 pandemic in the context of two markets: the newly established renminbi-denominated Shanghai International Energy Exchange in China and the US dollar-based Brent market in the United Kingdom.
Abstract: We examine the high-frequency intraday return and volatility transmission between crude oil futures prices and exchange rates during the 2020 Covid-19 pandemic in the context of two markets: the newly established renminbi-denominated Shanghai International Energy Exchange in China and the US-dollar-denominated Brent market in the United Kingdom. By controlling for the influence of the stock markets, our findings reveal significant disparities in return linkages, yet fairly comparable volatility transmission patterns. The International Energy Exchange shows no return linkages with exchange rates except before the shock, while Brent consistently shows return spillovers from crude oil futures prices to exchange rates. In both markets, the volatility spillovers from exchange rates to crude oil futures prices are unidirectional prior to the shock but become bidirectional as a result of the shock. Nevertheless, both the return and volatility spillover patterns in China resemble those in the United Kingdom when utilizing offshore instead of onshore exchange rates. Such similarities in return and volatility spillovers can also be observed during the 2022 Covid-19 shock that emerged in Shanghai. These findings have significant practical implications.
TL;DR: In this article , a generalized autoregressive conditional heteroscedasticity (GARCH) mixed data sampling (MIDAS) model with skewness and kurtosis was proposed for renminbi exchange rate volatility.
Abstract: We investigate the predictive value of time-varying higher moments and economic policy uncertainty (EPU) for renminbi exchange rate volatility. To do so we develop a generalized autoregressive conditional heteroscedasticity (GARCH) mixed data sampling (MIDAS) model with skewness and kurtosis (the GARCH-MIDAS-SK model), which accommodates time-varying non-Gaussianities (higher moments) of the renminbi exchange rate return distribution and allows us to link volatility to EPU. An empirical analysis based on daily USD/CNY exchange rate returns and monthly global EPU index data shows that the GARCH-MIDAS-SK-EPU model, which incorporates time-varying higher moments and global EPU, can yield more accurate out-of-sample renminbi exchange rate volatility forecasts than the various competing models (ie, the GARCH, GARCH-MIDAS, GARCH-MIDAS-EPU and GARCH-MIDAS-SK models). The superior predictive power of the GARCHMIDAS- SK-EPU model is robust to an alternative version of the global EPU index, alternative out-of-sample forecasting windows and local EPU indexes (Chinese EPU and US EPU). Our empirical findings highlight the value of incorporating timevarying higher moments and EPU into forecasts of renminbi exchange rate volatility.
TL;DR: This article investigated the synchronism and hysteresis between social and investor disagreements, underpinned by an analysis of millions of non-investment-oriented comments sourced from the Sina Weibo social media platform.
Abstract: The existing literature predominantly focuses on investor disagreements and their implications for financial returns, but it typically ignores the significant influence of noninvestors (ie, people who do not physically trade securities). In response to this oversight, our study investigates the synchronism and hysteresis between social and investor disagreements, underpinned by an analysis of millions of noninvestment-oriented comments sourced from the Sina Weibo social media platform. For the convenience of research and description, we introduce a novel metric – noninvestor disagreement – that captures this hysteresis. Further, when integrated into an asset pricing model, this metric demonstrates the ability to capture residuals that fall outside the purview of the Fama–French model. Building upon these findings, we outline several investment strategies that not only outperform the market, but also can be symbiotic with other investment strategies, suggesting an inclusive approach to strategic financial decision-making. In summary, this research augments the field of asset pricing and behavioral finance, offering a robust methodology for harnessing information typically overlooked by investors, thus enhancing the interpretive ability of asset pricing models.
TL;DR: In this article , the authors propose a dynamic program for valuing corporate securities under various Lévy processes, such as two jump diffusions and a pure-jump process, and compute and detail the total value of equity, the sum value of debt, and the aggregate value of the firm as well as the credit spreads of the debt by using Gaussian, double exponential and variance-gamma jump models.
Abstract: Most structural models for valuing corporate securities assume a geometric Brownian motion to describe the value of a firm’s assets. However, this does not reflect market stylized features: the default is more often driven by unexpected information and sudden shocks, which are not captured by the Gaussian model assumption. To remedy this, we propose a dynamic program for valuing corporate securities under various Lévy processes. Specifically, we study two jump diffusions and a pure-jump process. Under these settings, we build and experiment with a flexible framework that accommodates the balance-sheet equality, arbitrary corporate debts, multiple seniority classes, tax benefits and bankruptcy costs. While our approach applies to several Lévy processes, we compute and detail the total value of equity, the total value of debt and the total value of the firm as well as the credit spreads of the debt by using Gaussian, double exponential and variance-gamma jump models.
TL;DR: In this paper , the authors assess the maturation of target-date funds and their performance during the Covid-19 pandemic, when there were again significant market losses, and conclude that target date funds have largely met their designation and there is no evidence of them similarly gaming their asset allocation.
Abstract: 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.
TL;DR: In this paper , an alternative portfolio optimization framework is developed to handle this kind of information (given by an ordinal ranking of investment alternatives) and calculate an optimal capital allocation based on a Cobb-Douglas utility function.
Abstract: Attempts by investors to allocate capital across a selection of different investments are often hampered by the fact that their decisions are made with limited information (eg, no historical return data) and within an extremely limited time frame. In some cases, however, rational investors with enough experience are able to ordinally rank investment alternatives through relative assessments of the probabilities that such investments will be successful. However, in order 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 calculate an optimal capital allocation based on a Cobb–Douglas utility function (which we call the sorted weighted portfolio). With risk-neutral investors in mind, we show that the results of this portfolio optimization model usually outperform the output generated by the (intuitive) equally weighted portfolio of investment alternatives, which is the result of optimization when it is not possible to incorporate additional data (the ordinal ranking of the alternatives). We show that our model can further contribute to this area by helping risk-averse investors capture correlation effects.
TL;DR: In this paper , a systematic review of the literature on value-at-risk (VaR) models is presented, with the following two aims: to find the most used models in the literature and to verify whether their popularity has changed since the 2007-9 financial crisis.
Abstract: This paper presents a systematic review of the literature (SRL) on value-at-risk (VaR). More specifically, we review the models that have been applied to estimate VaR, with the following two aims: to find the most used models in the literature and to verify whether their popularity has changed since the 2007–9 financial crisis. The SRL is based on Scopus for the period from 1996 to 2017. Our results show that (generalized) autoregressive conditional heteroscedasticity models and extreme value theory, together with Monte Carlo simulation, historical simulation and variance–covariance, were the most used models. Since the crisis, the autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models have clearly been the most popular, while no significant difference has been found in the percentage of articles on the other models. This study can be considered the first SRL on VaR models because (to the best of our knowledge) no previous work of a similar nature has been carried out on this topic. This study provides a rich background for researchers and professionals interested in the topic, contributing detailed information about the papers published, classifying them by, for example, the model used, author(s), citation count, journals and year published.
TL;DR: In this article , a dual representation for an arbitrary mixture of convex risk measures is proposed, and the authors develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures.
Abstract: 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 of resulting risk measures from the properties of both alternative functionals and combination functions. We build on developing a dual representation for an arbitrary mixture of convex risk measures. In this case, we obtain a penalty that recalls the notion of inf-convolution under theoretical measure integration. We develop results related to this specific context. We also explore features of individual interest generated by our frameworks, such as the preservation of continuity properties and the representation of worst-case risk measures.
TL;DR: In this article , the authors analyzed the credit risk of commercial banks based on three risk factors using a vine copula and showed that banks that are not global systemically important banks are confronted with higher credit risk than global systemic important banks.
Abstract: Current research on commercial bank credit risk mainly focuses on the risk of loans and advances without considering the treasury operations and off-balance-sheet credit business that also carry credit risk. Ignoring the correlation between diverse risk factors results in the biased integration of credit risk. In this work, we aggregate the credit risk of commercial banks based on three risk factors using a vine copula. Our empirical results show that banks that are not global systemically important banks are confronted with higher credit risk than global systemically important banks. In addition, the risk of loans-and-advances business is positively correlated with treasury operations risk and negatively correlated with off-balance-sheet credit business. While the risk of loans-and-advances business is significant, the risks of treasury operations and off-balance-sheet credit business still play an indispensable role in (and contain vital information about) credit risk. In addition, banking as a system can achieve lower credit risk, but this effect is weakened under the extreme lower-tail risk.