Quarterly Review of Economics and Finance最新文献

We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018–2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021–2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns.

We examine how strategic interactions, i.e., strategic substitutes (SS) and strategic complements (SC), affect the sensitivity of cash savings to stock price. Using a panel data of U.S. firms over the 1997–2019 period, we show that cash savings are more sensitive to stock price among SS than SC and the intensity of rivals’ interactions in product market increases this sensitivity. These results are consistent with research showing that SS are more responsive to product market competition. We further show that R&D intensities and corporate hedging needs act as two channels through which strategic interactions affects cash holdings. First, R&D intensities increase the sensitivity of cash to stock price. This effect is more pronounced among SS, complementing researching showing stock market reacts more strongly to innovation by SS. Second, we report that hedging needs decrease the sensitivity of cash savings to stock price, especially for SC, consistent with the notion that using differentiated inputs increases hedging costs, hence, SC are more conservative in their liquidity management.

In the context of public deposit insurance organizational models, several interesting questions arise: Why does China's Deposit Insurance Corporation consistently lean toward the cooperative institution model, which is closely aligned with the central bank? Despite fervent advocacy for the independent institution model by the IADI and the U.S. Why does the unwavering stance exist? Is the choice of the cooperative institution model an "ignorant solution" or an "optimal solution" in China? Our work answers these questions for the first time, and we argue that it is the "optimal solution" that policymakers can choose after careful deliberation, not due to stupidity or inexperience. Based on the Honey Badger Algorithm, real options approach and expected loss pricing model, our work verifies the significant advantages of the cooperative institution model over the independent institution model in China. This pivotal distinction, primarily overlooked in the extant literature, suggests that universally accepted perspectives may not be ubiquitously relevant across all national contexts.

Our paper explores the existence and nature of an established relationship between the banks’ function as liquidity creators, their profitability, and the instability of the financial system in Lebanon. Using original, annual observations of Lebanese bank data for the period 1997 – 2019 and employing fixed effect OLS regressions and system GMM to account for the dynamic aspect of our data, we show that liquidity creation is significantly associated with lower financial stability and thus higher instability. Banks’ profitability is positively linked to their systemic stability. The results vary slightly from one estimate to another, but they stand up to robustness tests. Our empirical results have a substantial impact on banks’ control and regulatory approaches in Lebanon and similar developing countries, while contributing to a deeper understanding of systematic and broader financial instabilities in these countries.

Using Colombian credit vintage data, we decompose non-performing loans into two main components: one capturing the evolution of borrowers’ payment capacity and another reflecting changes in the credit risk assumed by banks when granting loans. We employ intrinsic estimators and penalized regression techniques to address the perfect multicollinearity inherent in the model. Our analysis reveals that these two components have evolved differently over time and that they interact with the real and credit cycles distinctively. In particular, we find that a favorable economic environment and loose financial conditions improve the payment capacity of borrowers to meet their obligations, but coincide with increased risk-taking by financial institutions. Finally, we advocate for the adoption of this decomposition as a policy tool, easily applicable by financial and economic authorities with access to a continuous flow of credit vintage data. This methodology facilitates the identification of credit risk origins, thereby informing economic policies aimed at mitigating systemic financial risks.

Over the last decade, the green bond market experienced strong growth rates fueled by the need to combat climate change. However, the discourse on enhancing the effectiveness of green bonds primarily revolves around regulatory measures, often overlooking the possibility of designing inherent incentives. We show that a green bond with a coupon structure positively related to the carbon price development stimulates (early) investment in an emission-reducing project and creates higher net present values ($NPV$s) when applied in project financing. In our simulation-based framework, we model carbon prices using a geometric Brownian motion, and create a general optimal stopping time problem regarding the start of the project. The green bond in our setting carries the risk of default, also mitigated by its carbon price-linked coupon structure.

We estimate stability performances in the Chinese banking industry over the 2007–2017 period using four risk indicators under nonparametric modelling. We are the first to calculate the risk indicator shadow prices, and we use a new way of studying the relationship between stability and economic performance. In particular, we reexamine stability performances when banks achieve their best economic performances. This questions the existence of stability rents, which form a prime reason for the banking authority to consider economic performance. Finally, we verify whether ownership has an impact on our results and investigate the role of the interest rate liberalization reforms.

We use intraday trading data from the Kraken exchange to calculate the daily price synchronicity of Bitcoin and Ether from February 2018 to December 2022. We then use a comprehensive report provided by christalblockchain.com to investigate the impact of hacks on price synchronicity between the top two cryptocurrencies. Our results show that price synchronicity, as measured by the realized correlation, is consistently positive throughout the sample period, with only one (negative) exception. We further uncover a positive relationship between hacking events and the future price synchronicity of Bitcoin and Ether. This result is robust to an alternative price synchronicity measure.

This study investigates the interplay between firm-level investor sentiment and uncertainty in financial markets. We demonstrate that investor sentiment significantly influences short-term stock market returns, particularly when there is an increase in firm-level uncertainty. This correlation becomes weaker among firms experiencing a decrease in uncertainty. The cross-sectional effect of sentiment is more pronounced during periods of heightened uncertainty, as evidenced by the higher returns of sentiment-based long-short portfolios under these conditions. Our findings are robust to adjusting for various factors and using alternative uncertainty and sentiment measures.

This paper predicts the daily return volatility of 28 advanced and developing stock markets using monthly metrics of the corresponding country and global energy-related uncertainty indexes (EUIs) recently proposed in the literature. Using data in their “natural” frequencies to avoid aggregation bias, the results show that country-specific and global EUIs have predictive powers for stock returns volatility for the in-sample periods, with increased levels of EUIs exhibiting the tendency to heighten volatility. This predictability also withstands various out-of-sample forecast horizons, implying that EUI is a statistically relevant predictor in the out-of-sample analysis. The forecast precision of the GARCH-MIDAS model is improved by incorporating global EUIs relatively more than country-specific EUIs. The robustness of the findings with respect to the choice of EUI and sample definition is further confirmed. The outcomes have important policy implications for the concerned stakeholders who are concerned with stability in the global financial system and economy.