1,721,000 research outputs found
Measure-invariance of copula functions as tool for testing no-arbitrage assumption
No full textCopulas, which are invariant under margins’ transforms induced by some change of measure, are investigated. It is emphasized that this particular kind of transforms induced by some change of measure, largely used in pricing techniques, preserves the invariance of the aggregation operator and a sufficient condition to assure it is proved. The discussion is extended to the time-preserving of measure-invariance; a characterization of its stability in time for multivariate stationary processes, based on the dynamic copula representation (see Cherubini et al., 2011), is provided. Finally a measure invariance-based statistical test for the absence of arbitrage opportunity assumption and its preservation in time is proposed and an empirical experiment based on quotes of S&P 500 futures and options traded on the Chicago Mercantile Exchange (CME) is discussed
Skewness, Basis Risk, and Optimal Futures Demand
No full textWe propose a maximum-expected utility hedging model with futures where cash and futures returns follow a bivariate skew-normal distribution, such to consider the effect of skewness on the optimal futures demand. Relative to the benchmark of bivariate normality, skewness has a material impact when the agent is significantly risk averse. Pure hedging demand is either greater or smaller than minimum-variance demand, depending on the relative skewness of cash and futures positions. The difference between pure hedging and minimum-variance demand increases with basis risk, i.e. the imperfect correlation between cash and futures returns. When the agent is moderately but not infinitely risk averse, there is room for speculative positions, and the optimal futures demand is driven by both basis risk and the expected return on the futures market
A distorted copula-based evolution model: risks’ aggregation in a Bonus–Malus migration system
Full text linkIn this paper, we put forward a new model to compute the loss distribution of an automobile insurance company’s portfolio evolving by a bonus–malus system. We allow for a continuous evolution of the demographic-economic system based on a migration’s rule which is refreshed in discrete time, i.e., at the monitoring times. Therefore, the migration’s probabilities are discretely updated through a technique based on the combinatorial distributions of claims’ arrival in the rating classes. This technique is hierarchical copula-based, a natural tool permitting us to represent the co-movement between claims’ arrivals and distorted due to the formalization of an arrival policy of claims, that restricts the set of combinatorial distributions to those representing the most probable scenarios, therefore distorting the loss function. At every monitoring date, the copula-based model computes the migration’s probabilities and the loss function which accommodates for a discrete-time dynamic of the claims’ reserving and the capital requirements. An empirical application, the evaluation of the claims’ reserving and the capital requirements for different kinds of hierarchies are analyzed, with real data originating with the General Insurance Association of Singapore
Climate risks and weather derivatives: A copula-based pricing model
No full textThe paper focuses on the role of climate and weather derivatives (CDs/WDs for short) as
instruments to hedge climate risk. The aim of this paper is hence twofold: (i) we introduce a
copula-based pricing methodology for multivariate CDs/WDs, whose flexible theoretical
framework allows to be suited to any pricing application and possible structure of multivariate
products, and (ii) we discuss the impact of CDs/WDs on climate risk and their implication for
financial stability.
Using the proposed framework, we illustrate a calibration example on a case study on data for
11 Italian cities, looking specifically at the joint hedge of rainfall (cumulated) and temperature
(either cumulated CDD or HDD, depending on the season), across four different seasons of the
year. We find that Archimedean copula functions characterized by left tail dependence (i.e.
Clayton copulas) are generally more suitable to fit the data, depending on the season and the
location. We also explore the advantages of using more sophisticated, i.e. rotated or
multivariate, copulas and assess the improvement of fitting. We find that rotated copulas
(specifically, a Rotated Gumbel) represent a first meaningful performance improvement; and
multivariate copulas such as the bi-parametric Survival BB8 significantly improve fitting, but
they also impose technical constraints due to the higher computational requirements.
Subsequently, leveraging both the theoretical model and the empirical results, we discuss the
relation between climate risk hedging and financial stability. Especially, we move from
modeling complexities and limitations to illustrate how incorrect calculations (in the form of
mispricings, or over/under estimations of capital at risk) can, alongside with climate change
effect, increase rather than reduce the climate physical risk and hence the concerns for financial
stability. Finally, we discuss this point in relation with the legislative framework, noting how,
in the current context of uncertain legislation and imperfect pricing, climate hedging risks are
likely to do more harm than good
Financing Sustainable Energy Transition with Algorithmic Energy Tokens
Full text linkFinancing energy firms and catalyzing the energy transition are pivotal for achieving a sustainable future. In this era of increasing environmental consciousness, banks are incorporating environmental considerations into their credit rating methodologies, like the Partnership for Carbon Accounting Financial Guidelines. In the meantime, the advent of digital tokens offers new avenues for energy token creation. This study establishes a factor model as the fundamental framework for algorithmic energy tokens and employs gradient-boosting tree regression to examine energy price drivers in Italy and Austria. The results underscore the heightened motivation to invest in energy transition and security during periods of elevated energy prices. Conversely, the drive to invest in clean energy sources diminishes when operational profits are low or energy security must be maintained. This research elucidates on an innovative financing solution that handles these dynamics, produces momentum, and focuses special emphasis on its potential for implementing environmental policies by developing an algorithmic energy token mechanism based on environmental regulations and considerations
The impact of the dependence structure in risk management: a focus on credit-risk
No full textThe aim of the paper is to discuss the important role of the dependence
structure in risk management. Therefore, we focus on creditrisk
and propose an innovative model to value the credit risk of a
portfolio. This new approach (HYC for short) is based on a hierarchical
hybrid copula and involves a clusterization of the portfolio in several
risk’s classes. The HYC model is classified as hybrid because the computation
of the loss cdf depends on the class’s cardinality: for large
groups one is justified to apply a limiting approach, while for small
ones one applies a procedure preserving the granularity of the group
itself. In order to appreciate the impact of the dependence structure
in credit-risk evaluation, a VaR analysis based on the HYC loss function
is here compared to the CreditMetrics approach in an in-sample
exercise and to the empirical VaR in an out-of sample exercise aimed
to test the forecasting effectiveness of the model. This comparison
allows us to appreciate over/under-valuation of the capital detained
from the financial institution. Moreover, the impact of an enlargement
of the dependence structure is discussed with respect to the
systemic/contagious effects in the context of a portfolio optimisation
with constraint on a sub-portfolio’s risk
Hedging the Financial Risk of Water Scarcity: The Use of Weather Derivatives
No full textWater management has evolved beyond a purely engineering challenge, as climate change-induced water shortages significantly impact even the socio-economic systems of the world's wealthiest countries. This chapter introduces two innovative types of weather derivatives designed to hedge against the volumetric risk associated with water scarcity, whether due to light rainfalls or critically low basin levels necessitating public rationing. The first derivative is a European-style Quanto put option, financially settled to mitigate losses from insufficient rainfall. The second is a European digital cash-or-nothing option, tailored to address the depletion of water resources in specific basins. A detailed case study on Trinity Lake in California, USA, demonstrates the effectiveness of these instruments in protecting against the detrimental effects of water shortages
Counting statistics for dependent random events: with a focus on finance
No full textThis book on counting statistics presents a novel copula-based approach to counting dependent random events. It combines clustering, combinatorics-based algorithms and dependence structure in order to tackle and simplify complex problems, without disregarding the hierarchy of or interconnections between the relevant variables. These problems typically arise in real-world applications and computations involving big data in finance, insurance and banking, where experts are confronted with counting variables in monitoring random events. In this new approach, combinatorial distributions of random events are the core element. In order to deal with the high-dimensional features of the problem, the combinatorial techniques are used together with a clustering approach, where groups of variables sharing common characteristics and similarities are identified and the dependence structure within groups is taken into account. The original problems can then be modeled using new classes of copulas, referred to here as clusterized copulas, which are essentially based on preliminary groupings of variables depending on suitable characteristics and hierarchical aspects. The book includes examples and real-world data applications, with a special focus on financial applications, where the new algorithms’ performance is compared to alternative approaches and further analyzed. Given its scope, the book will be of interest to master students, PhD students and researchers whose work involves or can benefit from the innovative methodologies put forward here. It will also stimulate the empirical use of new approaches among professionals and practitioners in finance, insurance and banking
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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