Annals of Finance最新文献

The paper investigates the complementary role of hard and soft information in affecting the bankruptcy outcome of in-court procedures. Previous literature mostly focuses on hard information as driver of the bankruptcy outcome. In a bankruptcy context, we identify the causes of default as a key piece of soft information which can emerge through a textual analysis of the legal papers written by the insolvency practitioners. We posit that soft information complements hard information in guiding creditors’ choice of the bankruptcy outcome. To test our hypotheses, we construct a unique dataset composed of hard and soft information of Italian Small and Medium Enterprises that faced in-court debt renegotiation between 2011 and 2016. We show that the role of hard information in guiding creditors’ decisions depends on the specific cause of default they interact with and we conclude that the two sets of information jointly shape the conditions for the bankruptcy outcome.

We create a continuous-time setting in which to investigate how the management of a firm controls a dynamic choice between two generic voluntary disclosure decision rules (strategies) in the period between two consecutive mandatory disclosure dates: one with full and transparent disclosure termed candid, the other, termed sparing, under which values only above a dynamic threshold are disclosed. We show how parameters of the model such as news intensity, pay-for-performance and time-to-mandatory-disclosure determine the optimal choice of candid versus sparing strategies and the optimal times for management to switch between the two. The model presented develops a number of insights, based on a very simple ordinary differential equation characterizing equilibrium in a piecewise-deterministic model, derivable from the background Black–Scholes model and Poisson arrival of signals of firm value. It is shown that in equilibrium when news intensity is low a firm may employ a candid disclosure strategy throughout, but will otherwise switch (alternate) between periods of being candid and periods of being sparing with the truth (or the other way about). Significantly, with constant pay-for-performance parameters, at most one switching can occur.

We consider approximate pricing formulas for European options based on approximating the logarithmic return’s density of the underlying by a linear combination of rescaled Hermite polynomials. The resulting models, that can be seen as perturbations of the classical Black-Scholes one, are nonpararametric in the sense that the distribution of logarithmic returns at fixed times to maturity is only assumed to have a square-integrable density. We extensively investigate the empirical performance, defined in terms of out-of-sample relative pricing error, of this class of approximating models, depending on their order (that is, roughly speaking, the degree of the polynomial expansion) as well as on several ways to calibrate them to observed data. Empirical results suggest that such approximate pricing formulas, when compared with simple nonparametric estimates based on interpolation and extrapolation on the implied volatility curve, perform reasonably well only for options with strike price not too far apart from the strike prices of the observed sample.

In this paper, we analyze the robustness and sensitivity of various continuous-time rough Volterra stochastic volatility models in relation to the process of market calibration. Model robustness is examined from two perspectives: the sensitivity of option price estimates and the sensitivity of parameter estimates to changes in the option data structure. The following sensitivity analysis consists of statistical tests to determine whether a given studied model is sensitive to changes in the option data structure based on the distribution of parameter estimates. Empirical study is performed on a data set consisting of Apple Inc. equity options traded on four different days in April and May 2015. In particular, the results for RFSV, rBergomi and (alpha )RFSV models are provided and compared to the results for Heston, Bates, and AFSVJD models.

This paper shows how to uniquely price non-traded assets using no-arbitrage in an otherwise frictionless market setting. The approach requires the assumption that the hedging error, properly defined, is non-priced or idiosyncratic risk. This methodology can be applied to private loans, illiquid publicly traded debt, insurance contacts, private equity, real estate, and real options.

Bitcoin enthusiasts argue that it is free from central banks decisions and it is a hedge against inflation. Using high-frequency monetary surprises associated with decisions made by the Fed and the ECB, I show that these claims are not supported by the data. Bitcoin systemically reacts to monetary and central bank information shocks. I find that these reactions vary over time: not only by changing the magnitude but sometimes sign of reaction. Fed’s disinflationary shocks increase Bitcoin price, while the ECB’s decrease, hence providing little support for it as an inflation hedge.

This work utilizes the fractional Black–Scholes model to estimate the option-implied Hurst exponents, interpreted as forward-looking expectations of return persistence. The focus of the paper is on how corresponding believes enter into factor based asset pricing models. Empirical analyses are carried out for the cross-section of S &P 500 stocks. We make the important observations that (i) stock returns show significant patterns of time-varying persistence and (ii) corresponding believes are reflected within option prices. Incorporating the Hurst exponents allows us to split up CAPM betas into pure market correlation risk (around 70–80%) and into excess persistence believes (about 20–30% of the risk loading). A direct comparison to standard CAPM shows that incorporating persistence believes significantly improves the predictability of future realized returns, and partially releases the beta anomaly. The effects become even stronger the greater the prediction horizon. Hence, the concept of fractal motions enables a deeper understanding of risk structures without the need of additional risk factors.

We propose sentiment-based indicators of real estate market stress for the USA, the UK, Canada, Australia, India, and on the global scale. The global and country-level indicators are based on a novel methodology synthesizing textual analysis of real estate research and Google search data. Using mixed frequency vector autoregressions, we show that in the USA, the UK, Australia and India, the sentiment-based indicators are found to mediate the relationship between real estate prices and systemic financial risk. In particular, for the UK, there is a vicious circle involving the interaction among the three variables: the sentiment-based indicator of real estate market stress unidirectionally leads systemic risk, the latter impacts real estate prices, whereas the prices drive the stress sentiment. Canada appears the only sample country where real estate market stress sentiment is unrelated to real estate prices and systemic risk. On the global scale, there is a bi-directional linkage between the stress sentiment and real estate prices. Overall, our empirical findings suggest that policymakers and real estate market participants should account for sentiment regarding real estate market stress in their decision-making.

In this paper, a general framework is developed for continuous-time financial market models defined from simple strategies through conditional topologies that avoid stochastic calculus and do not necessitate semimartingale models. We then compare the usual no-arbitrage conditions of the literature, e.g. the usual no-arbitrage conditions NFL, NFLVR and NUPBR and the recent AIP condition. With appropriate pseudo-distance topologies, we show that they hold in continuous time if and only if they hold in discrete time. Moreover, the super-hedging prices in continuous time coincide with the discrete-time super-hedging prices, even without any no-arbitrage condition.

Systemic risk is a complex and multifaceted phenomenon that needs to be addressed from different perspectives. In this work we propose a Compositional Data (CoDa) approach to analyze the distribution of relative contributions to systemic risk associated with major European countries during the period 2008–2021. We represent systemic risk measures corresponding to those countries as percentage shares, or parts, of a compositional dataset and we perform a multivariate statistical analysis using specific CoDa procedures. The proposed approach sheds new light on some variability patterns and cross-country relationships that appear to be linked to the composition of systemic risk parts in the system.