Ricerc@Sapienza

We investigate the application of natural hedging strategies for long-term care
(LTC) insurers by diversifying both longevity and disability risks affecting LTC
annuities. We propose two approaches to natural hedging: one built on a multivariate
duration, the other on the Conditional Value-at-Risk minimization of
the unexpected loss. Both the approaches are extended to the LTC insurance
using a multiple state framework. In order to represent the future evolution of
mortality and disability transition probabilities, we use the stochastic model of

Following the research strands of enhanced index tracking and of portfolio performance measures optimization, we propose to choose, among the feasible asset portfolios of a given market, the one that maximizes the geometric mean of the differences between its risk and gain and those of a suitable reference benchmark, such as the market index. This approach, which has a peculiar geometric interpretation and enjoys remarkable features, provides the efficient portfolio that dominates the largest amount of portfolios dominating the reference benchmark index.

In my paper, I examine the role that models play, and the relation between models and data, in finance, in particular in stock markets. I discuss several dangerous liaisons between models and data both from a theoretical and a practical viewpoint and their effects on the behaviour of the financial systems and their actors. I argue that these relations and liaisons defy the way traditional philosophy of science accounts for models and the relation between models and data, as stock markets exhibit several dynamics and features that do not fit them.

I examine the role that mathematics plays in understanding and modelling finance, especially stock markets, and how philosophy affects it. To this end, I explore how mathematics penetrates finance via physics, constructing a ‘financial physics’, and I outline the philosophical backgrounds of this process, in particular, the ‘philosophy of equilibrium’ and that of critical points or ‘out-of-equilibrium’.

The book offers an interdisciplinary perspective on finance, with a special focus on stock markets. It presents new methodologies for analyzing stock markets’ behavior and discusses theories and methods of finance from different angles, such as the mathematical, physical and philosophical ones. The book, which aims at philosophers and economists alike, represents a rare yet important attempt to unify the externalist with the internalist conceptions of finance.

The nature of the data of financial systems raises several theoretical and methodological issues, which not only impact finance, but have also philosophical and methodological implications, viz. on the very notion of data. In this paper I will examine several features of financial data, especially stock markets data: these features pose serious challenges to the interpretation and employment of stock markets data, weakening the ‘myth of data’. In particular I will focus on two issues: (1) the way data are produced and shared, and (2) the way data are processed.

The view from outside on finance maintains that we can make sense of, and profit from, stock markets’ behavior, or at least few crucial properties of it, by crunching numbers and looking for patterns and regularities in certain sets of data. The basic idea is that there are general properties and behavior of stock markets that can be detected and studied through mathematical lens, and they do not depend so much on contextual or domain-specific factors.

The view from inside maintains that not only to study and understand, but also to profit from financial markets, it is necessary to get as much knowledge as possible about their internal ‘structure’ and machinery. This view maintains that in order to solve the problems posed by finance, or at least a large part of them, we need first of all a qualitative analysis. Rules, laws, institutions, regulators, the behavior and the psychology of traders and investors are the key elements to the understanding of finance, and stock markets in particular.

Our paper derives and estimates a New Keynesian wage Phillips curve that accounts for intrinsic inertia. Our approach considers a wage-setting model featuring an upward-sloping hazard function, that is based on the notion that the probability of resetting a wage depends on the time elapsed since the last reset. According to our specification, we obtain a wage Phillips curve that also includes backward-looking terms, which account for persistence. We test the slope of the hazard function using GMM estimation.