Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

We present a neural network-based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models—including second-generation stochastic volatility models and the rough volatility family—and a range of derivative contracts. Neural networks in this work are used in an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. The form in which information from available data is extracted and used influences network performance: The grid-based algorithm used for calibration is inspired by representing the implied volatility and option prices as a collection of pixels. We highlight how this perspective opens new horizons for quantitative modelling. The calibration bottleneck posed by a slow pricing of derivative contracts is lifted, and stochastic volatility models (classical and rough) can be handled in great generality as the framework also allows taking the forward variance curve as an input. We demonstrate the calibration performance both on simulated and historical data, on different derivative contracts and on a number of example models of increasing complexity, and also showcase some of the potentials of this approach towards model recognition. The algorithm and examples are provided in the Github repository GitHub: NN-StochVol-Calibrations.     

Agent-based modeling of systemic risk in the European banking sector

Journal of Economic Interaction and Coordination - In this paper, we use an agent-based simulation combined with innovative calibration techniques to model the European banking system as accurately...     

FIRM SIZE AND STOCK RETURNS: A QUANTITATIVE SURVEY

Firm size is commonly used in numerous empirical asset pricing models as a determinant of expected stock returns. Yet there is little consensus over the magnitude and stability of the size premium. In fact, some researchers even question whether firm size should be used as a pricing factor. We collect 1746 estimates of the slope coefficients capturing the association between firm size and stock returns reported in 102 published studies and conduct the first meta‐analysis on the size premium. We find evidence of a strong bias toward publishing statistically significant negative slope coefficients. After correcting for the bias, we find that the literature implies a difference in annual stock returns on the smallest and the largest New York Stock Exchange (NYSE) market capitalization quintiles of 1.72%. For the time periods covered in the sampled articles, we find that the size premium was larger in earlier years and that the intensity of publication bias has been decreasing over time.     

Chaebol-affiliated analysts: Conflicts of interest and market responses

Some Korean business groups, or chaebols, have a large stake in securities firms that issue analysts’ reports on their member companies. This structure is unique in that industrial companies and securities firms are affiliated and operate within the same group. We investigate the informational content of earnings forecasts, stock recommendations and target prices made by the chaebol-affiliated analysts, using data collected between 2000 and 2008. The chaebol analysts tend to make more optimistic earnings forecasts for the member companies. The mean EPS forecast error (5.36%) of the affiliated analysts for the same chaebol company are significantly larger than that (3.23%) of other chaebol and independent analysts. The chaebol analysts also assign better recommendations by almost one level and set target prices 2.5% higher to the member companies after controlling for company and analyst characteristics. These results are consistent with the hypothesis that chaebol analysts’ reports are biased by conflicts of interest. Stock market reactions do not differ in response to announcements of stock recommendations issued by affiliated vs. non-affiliated analysts. This suggests that capital markets do not recognize the conflicts of interest inherent in chaebol analysts’ reports.     

R&D management through network mapping: using the Internet to identify strategic network actors in cooperative research networks

Most R&D management techniques to date have emphasized the immediate organizational context or the internal structure and process of the R&D project. Recently however attempts have been made at situating R&D management in a wider context of inter‐organizational R&D activities. This paper presents the technique of R&D network mapping as a means of providing strategic intelligence to the R&D manager. The paper backgrounds this technique with a discussion of the general issue and significance of R&D networks. A methodology for R&D network mapping is then exemplified by outlining how a network database of cooperating projects and institutes in the telecom research sector was built by utilizing the World Wide Web. Finally a number of suggestions are made as to how and when network mapping should be applied in strategic R&D management.     

American stochastic volatility call option pricing: A lattice based approach

This study presents a new method of pricing options on assets with stochastic volatility that is lattice based, and can easily accommodate early exercise for American options. Unlike traditional lattice methods, recombination is not a problem in the new model, and it is easily adapted to alternative volatility processes. Approximations are developed for European C.E.V. calls and American stochastic volatility calls. The application of the pricing model to exchange traded calls is also illustrated using a sample of market prices. Modifying the model to price American puts is straightforward, and the approach can easily be extended to other non-recombining lattices.     

Innovativeness: Its antecedents and impact on business performance

In this study, we address three research questions: (1) Why are some industrial firms more innovative than others? (2) What effect does innovativeness has on business performance? (3) Does the linkage between innovativeness and business performance depend on the environmental context? Accordingly, we draw on various theoretical perspectives to develop hypotheses that propose market orientation, entrepreneurial orientation, and learning orientation as key antecedents to innovativeness, as well as a direct relationship between innovativeness and business performance. A model is devised and tested that examines these relationships in general and in the context of varying market turbulence. Findings confirm the validity of the model and afford various insights on the role of market turbulence in the proposed relationships. Lastly, implications are offered on the antecedents and consequences of organizational innovativeness.     

Leadership, the buying center, and supply chain performance: A study of linked users, buyers, and suppliers

With conceptual foundations taken from leadership theory and the resource-based view (RBV), this study examines the influence of transactional and transformational leadership on the relationship between the value of the corporate buying center and performance in supply chains. The sample consists of 58 directly linked and matched supply chains, each composed of one user (internal customer), one corporate buyer, and one external supplier. The results indicate that transformational leadership has a positive moderating effect on the relationship between the value of the corporate buying center and performance, while transactional leadership negatively moderates this relationship. Two “localness” dimensions (formalization and centralization) and two “openness” dimensions (participative and reflective) were included as controls in the analysis.     

Examining a key corporate role: The influence of capital allocation competency on business unit performance

Research on the role of the corporate office in firm performance has focused on establishing how much performance variance can be attributed to a “corporate effect,” with little attention devoted to understanding how this influence occurs. In this study, we model capital allocation competency as a dynamic managerial capability and find that lower levels of allocation competency in the form of excess investment to business units with relatively poorer future prospects reduce business unit performance. We also find that market conditions affect performance implications of capital allocation—allocation competency is more salient in more competitive markets. These results enhance our understanding of how the corporate office influences business unit performance through its role in allocating capital across business units. Copyright © 2014 John Wiley & Sons, Ltd.     

An application of finite elements to option pricing

This study applied the finite element method (FEM) to pricing options. The FEM estimates the function that satisfies a governing differential equation through the assembly of piecewise continuous functions over the domain of the problem. Two common representations, a variational functional representation, and a weighted residual representation are used in the application of the method. The FEM is a versatile alternative to other popular lattice methods used in option pricing. Advantages include the abilities to directly estimate the Greeks of the option and allow nonuniform mesh construction. As an illustration of the advantages that the FEM offers, the method was used to price European put options and discrete barrier knock‐out put options. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:19–42, 2001