Journal of Financial Stability最新文献

Decentralized Autonomous Organization (DAO) is very popular in Decentralized Finance (DeFi) applications as it provides a decentralized governance solution through blockchain. We analyze the governance characteristics in the Maker protocol, its stablecoin DAI and its governance token Maker (MKR). To achieve that, we establish several measurements of centralized governance. Our empirical analysis investigates the effect of centralized governance over a series of factors related to MKR and DAI, such as financial, network and Twitter sentiment indicators. Our results show that governance centralization influences the Maker protocol and that the distribution of voting power matters. The main implication of this study is that centralized governance in MakerDAO very much exists, while DeFi investors face a trade-off between decentralization and performance of a DeFi protocol. This further contributes to the contemporary debate over whether DeFi can be truly decentralized.

We examine implications of a central bank digital currency (CBDC) for banks using business models particularly dependent on customer deposits. Employing unique customer data hand-collected from German savings and cooperative banks, we generate conversion rates for deposits into a CBDC. Even at moderate conversion rates, most banks would have experienced funding problems and lost profits if a CBDC had been introduced in most years from 2000 onward. Our results are relevant for commercial banks, contributing to better assessments of the impact of CBDCs on liquidity and profitability and help central banks to identify implementation costs for banks within historical and hypothetical interest rate environments.

Despite high volatility, Bitcoin is known to offer diversification benefits through its relatively low correlation with stock markets. Unlike traditional safe-haven assets, Bitcoin prices strongly respond to time-varying correlations and diversification benefits. We find that a decrease (an increase) in correlation between Bitcoin and S&P500 index returns strongly predicts higher (lower) Bitcoin returns the next day. Under the classical mean–variance framework, we develop a stylized model of Bitcoin prices utilizing extreme disagreement among heterogeneous Bitcoin investors. When our model is calibrated to the observed predictability of Bitcoin returns, the model simultaneously explains the lack of predictability in traditional safe-haven assets such as gold and long-term treasuries.

We provide a systematic classification and evaluation of the different types of digital currencies. We express skepticism regarding centralized digital currencies and focus our economic analysis on private digital currencies. We specifically highlight the potential for private digital currencies to improve welfare within an emerging market with a selfish government. In that setting, we demonstrate that a private digital currency not only improves citizen welfare but also encourages local investment and enhances government welfare. The fact that a private digital currency enhances government welfare implies a permissive regulatory policy which enables citizens to realize the previously referenced welfare gains.

This paper uses a stylized simulation model to assess the potential impact of climate transition risk on banks’ balance sheets in a climate-stress-testing (i.e. short-run) framework. We show that a moderate to high transition risk increases overall bank losses only relatively modestly if the baseline is a stressed macroeconomic scenario. However, even in a benign macroeconomic scenario, if high-carbon assets are at least 13% riskier than comparable assets a fire sale mechanism could amplify an initially contained shock into a systemic crisis, resulting in significant losses for the EU banking sector. We show that transition risks are concentrated, and find that an additional capital buffer of 0.9% risk-weighted assets on average would be sufficient to protect the system.

This paper quantifies the Too-Systemic-To-Fail (TSTF) paradigm in the Eurozone since the introduction of the Euro through three primary dimensions: Too-Big-To-Fail (TBTF), Too-Interconnected-To-Fail (TITF), and Too-Many-To-Fail (TMTF). We apply prominent systemic risk measures based on public data, including the Granger-causality network (GCN), Delta Conditional Value-at-Risk (ΔCoVaR), Marginal Expected Shortfall (MES), and Systemic Risk Index (SRISK). Financial interconnectedness and systemic risk exposure within the 17-member states of the Eurozone are measured on two levels: (i) identifying which financial sectors (banking, diversified financials, insurance, and real estate) are most exposed to systemic risk in the Eurozone at the union level; and (ii) identifying which member state is most exposed to systemic risk within each financial sector at the country level. We extend the original ΔCoVaR, MES and SRISK models by incorporating the bootstrap Kolmogorov-Smirnov stochastic dominance test to rank institutions based on their exposure to systemic risk formally.

We investigate the time variation in credit rating standards awarded to financial institutions of commercial bank credit ratings awarded by the three principal CRAs from 1990 to 2015 in a world-wide context by testing for well-defined structural shifts. We focus on the part of the ratings that cannot be accounted using publicly available information. We test whether major financial events are conditioning, ex-post such changes Distinctively in this paper’s timespan our analysis covers four periods: (i) before and (ii) after the 2001–2 corporate collapses, followed by (iii) before the global financial crisis and (iv) after the global financial crisis. We find substantial differences in the assignment of bank credit ratings among the three major agencies, Moody’s, Fitch, and S&P. Agencies differ both in terms of re-adjustment of ratings but also on the speed of response to the evens. All three agencies tightened ratings during the 2008 crisis and kept reducing them in its aftermath.

We provide a joint non-parametric test to gather insights on the boundaries of applicability of Stochastic Discount Factor (SDF) models. We find that a non-trivial class of models cannot price the U.S. stock market equally weighted portfolio, implying non-monotonic SDFs, especially over the last 50/60 years in (recessionary) periods characterized by higher market volatility. Stocks responsible for this rejection mostly belong to the smallest NYSE market cap decile, are characterized by high idiosyncratic risk, and typically cannot be priced via SDF models where the aggregate level of risk aversion is bigger then 9 or 10. Excluding these stocks increases the ability to explain the cross-section of returns without impairing the ability to span the mean–variance frontier.

Strategic alliances are voluntary corporate arrangements for mutual benefit. Although alliances are common as an alternative to M&As, they require cooperation between alliance partners who continue to operate as independent companies. Thus, relational risk—the probability and consequences of unsatisfactory cooperation or opportunistic behavior—is inherent in alliances and a major determinant of alliance success. In this paper, we examine and find that alliance announcement CARs are higher for companies sharing the same auditor with their alliance partner. Further, our findings suggest that the shared auditor effect is stronger for alliances where potential relational risk between alliance partners is greater. Our findings hold when we use “withdrawn” (i.e., the withdrawal of an announced alliance before its start date) as an alternative, albeit inverse, measure of alliance success. Collectively, we provide novel evidence which suggests that auditors add shareholder value by playing a matchmaking role in alliance formation, building inter-company trust and mitigating relational risk by facilitating the sharing of non-financial information between potential alliance partners among their audit clients.

We present evidence that firms with greater asset redeployability are more likely to engage in green innovation activities, as measured by corporate green patents and citations. This finding supports the notion of funding flexibility and withstands numerous robustness and endogeneity tests. The relationship is particularly pronounced for firms facing high climate change uncertainty, as well as those in high-polluting industries. Moreover, we find further evidence that the pursuit of green innovation is associated with improved firm value over the long term. These insights shed light on how asset redeployability correlates with both innovation outcomes and firm performance within the context of green finance for sustainable development.