Knowledge that Transforms

We examine whether connected hedge funds (i.e. those that are prime-brokerage clients of bailout banks) benefited from bailout programs initiated in seven countries during the 2007–2009 financial crisis. We find that being connected to a bailout bank is generally beneficial for hedge funds in that it lowers the rate of fund failure. However, this benefit becomes smaller during the post bailout period, for example, due to the greater risk-taking and higher leverage of such funds subsequent to bailouts. As such, our findings provide support for the moral hazard hypothesis.

Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in the implied volatility surfaces of the most actively traded commodity options between 2006 and 2016. Adopting a rolling out-of-sample forecasting framework that addresses the common multiple comparisons problem, we establish that, for energy and precious metals options, explicitly modeling the term structure of implied volatility using the Nelson-Siegel factors produces the most accurate forecasts.

We examine the impact of the media on firms’ leverage adjustments. Using a comprehensive sample of global news across 33 countries, we find that greater news coverage and more positive news sentiment are associated with greater leverage adjustment speeds. This finding is consistent with the argument that media coverage and content help lower the cost of firms’ adjustment toward target leverage. We further find evidence supporting two mechanisms through which the news media affects leverage adjustments: information dissemination and monitoring. Overall, our results are consistent with the dynamic trade-off theory of capital structure.

This paper examines the relation between geographic diversification and credit risk in microfinance. The empirical findings from the banking industry are mixed and inconclusive. This study extends the discussion into a new international setting: the global microfinance industry with lenders having both social and financial objectives. Using a large global sample of microfinance institutions (MFIs), we find that geographic diversification comes with more credit risks. However, this finding is more pronounced among non-shareholder MFIs like NGOs and cooperatives, compared to shareholder-owned MFIs. Moreover, the results show that MFIs can mitigate the effect of geographic diversification on risk with group lending methodology.

This paper explores whether firms manage their earnings after stock splits to meet the raised expectations from the market due to the positive signal sent by the splits. We first document that post-split drift mainly exists in the first three months and is positively associated with post-split standardized unexpected earnings (SUE). However, the higher post-split SUE of split firms is associated with higher discretionary accruals and abnormally lower R&D expenses. This result is consistent with our hypothesis that split firms overstate their post-split earnings by manipulating accruals and reducing R&D spending. Moreover, post-split abnormal returns increase with discretionary accruals and R&D reduction for about six months and tend to reverse over longer horizons, especially for firms with negative pre-split SUE. Overall, our results indicate that the post-split drift is a short-term phenomenon and partly attributable to the earnings management after the splits.

This paper revisits the relationship between debt and growth from a vantage point that considers the totality of private and public debt. We exploit quarter-long timing lags inherent in the response of borrowing to innovations in output to identify the effects of debt on growth in a panel vector autoregressive model. We verify that debt accumulation is negatively related to output growth, with a one standard deviation innovation in the former leading to a 0.2 percentage-point contraction in the latter. This result is robust to the inclusion of exogenous variables in the system, alternative measures of the endogenous variables, and varying temporal treatments. We also find variations depending on the type of debt accumulated, the specific subset of countries considered, and the channels along which debt expansion operates.

By investigating the influence of negative interest rate policy (NIRP) on bank margins and profitability, this paper identifies country- and bank- specific characteristics that amplify or weaken the effect of NIRP on bank performance. Using a dataset comprising 7,359 banks from 33 OECD member countries over 2012–16 and a difference-in-differences methodology, we find that bank margins and profits fell in NIRP-adopter countries compared to countries that did not adopt the policy. Moreover, this adverse NIRP effect depends on bank specific-characteristics such as size, funding structure, business models, assets repricing and product – line specialization. The effectiveness of the pass-through mechanism of NIRP can also be affected by the characteristics of a country's banking system, namely, the level of competition and the prevalence of fixed/floating lending rates.

In this paper, I show that CEO power, which arises from differences in national culture, can weaken a firm’s governance. Based on a hand-collected dataset with more than 5000 forced and voluntary CEO transitions across 37 countries, I find that CEOs are less likely to be dismissed for bad performance in more hierarchical countries. The results are robust to alternative measures of hierarchy, a large battery of control variables, subsample analysis, placebo tests, and different empirical methodologies. Stronger hierarchies also allow for idiosyncratic managerial styles around exogenous turnover events of CEOs. Overall, the results suggest that the power and importance of CEOs vary across countries.

We study the probabilistic properties of the fractional Ornstein–Uhlenbeck process, which is a relevant framework for volatility modeling in continuous time. First, we compute an expression for its variance for any value of the Hurst parameter, H ∈ (0, 1). Second, we derive the density of the process and we calculate the probability of its supremum to be above a given threshold. We provide a number of illustrations based on fractional stochastic volatility models, such as those of Comte and Renault (1998), Bayer et al. (2016) and Gatheral et al. (2018). Finally, the empirical analysis, based on the realized variance series of S&P500, shows the usefulness of these theoretical results for risk management purposes, especially when a characterization of the volatility tail risk is needed.

Previous studies identify default rate as the main systematic determinant of bond recovery rates. We revisit this paradigm by investigating the impact of another factor, economic uncertainty. Based on a wide sample of American default issues and relying on beta regression models, well-suited for the bounded, heteroskedastic and skewed sample of recovery rates, we analyze the determinants of recovery rate distributions. We find economic uncertainty to be of paramount importance, as it proves to be the most important systematic determinant of recovery rate distributions, significant for both their mean and dispersion. By contrast, default rate remains a key determinant of the dispersion of these distributions, but not for their means. Considering this evidence is critical to the sound implementation of stochastic recovery rate models used by financial institutions for the computation of regulatory capital.