The recursive nature of KVA: KVA mitigation from KVA

KVA represents the extra cost being charged by banks to clients in order to remunerate banks’ shareholders for the mandatory regulatory capital provided by them throughout the life of the deal. Therefore, KVA represents earnings charged to clients that must be retained in the bank’s balance sheet and not be immediately paid out as dividends. Since retained earnings are part of core TIER I capital, future KVAs imply a deduction in today’s KVA calculation. In this paper we propose a KVA formula that is consistent with his idea and in line with full replication of market, ounterparty and funding risks. Although the formula might seem cumbersome at first sight due to its recursive nature, we show how calculate it in a Montecarlo XVA engine without any approximation. Finally, we provide a numerical example where the KVA obtained under this new formula is compared with other approaches yielding significantly lower adjustments

The recursive nature of KVA: KVA mitigation from KVA

KVA represents the extra cost being charged by banks to clients in order to remunerate banks’ shareholders for the mandatory regulatory capital provided by them throughout the life of the deal. Therefore, KVA represents earnings charged to clients that must be retained in the bank’s balance sheet and not be immediately paid out as dividends. Since retained earnings are part of core TIER I capital, future KVAs imply a deduction in today’s KVA calculation. In this paper we propose a KVA formula that is consistent with his idea and in line with full replication of market, ounterparty and funding risks. Although the formula might seem cumbersome at first sight due to its recursive nature, we show how calculate it in a Montecarlo XVA engine without any approximation. Finally, we provide a numerical example where the KVA obtained under this new formula is compared with other approaches yielding significantly lower adjustments

A retained earnings consistent KVA approach and the impact of taxes

KVA represents the extra cost being charged by banks to non collateralized counterparties in order to remunerate banks' shareholders for the mandatory regulatory capital provided by them throughout the life of the deal. Therefore, KVA represents earnings charged to clients that must be retained in the bank's balance sheet and not be immediately paid out as dividends. Since retained earnings are part of core TIER I capital, future KVAs imply a deduction in today's KVA calculation. Another key component of KVA is the fact that shareholder's returns (dividends and capital gains) are generated after taxes are paid. Therefore, taxes should be reflected in the KVA formula. By treating KVA as retained earnings, we derive a pricing formula that is consistent with full replication of market, counterparty and funding risks, and that takes the effect of taxes into account. We provide a numerical example where the KVA obtained under this new formula is compared with other approaches yielding significantly lower adjustments. This numerical example also helps us to assess the relevance of taxes

Pricing Derivatives in the New Framework: OIS Discounting, CVA, DVA & FVA

As a byproduct of the 2007-2008 credit crunch, derivatives pricing and risk management are experiencing a dramatic transformation. Assumptions that were widely accepted not long ago, like absence of counterparty credit risk and the existence of a unique risk free curve available for every derivatives hedger in the derivatives replication process, are no longer accepted. Financial institutions are changing the way in which counterparty credit risk and funding risk are managed. We find ourselves in a world with multiple discounting curves for any given currency and with different adjustments to apply to the price of financial derivatives that seem difficult to hedge. The target of this book is to make a deep review of how these effects impact the derivatives valuation theory

Pricing Derivatives in the New Framework: OIS Discounting, CVA, DVA & FVA

As a byproduct of the 2007-2008 credit crunch, derivatives pricing and risk management are experiencing a dramatic transformation. Assumptions that were widely accepted not long ago, like absence of counterparty credit risk and the existence of a unique risk free curve available for every derivatives hedger in the derivatives replication process, are no longer accepted. Financial institutions are changing the way in which counterparty credit risk and funding risk are managed. We find ourselves in a world with multiple discounting curves for any given currency and with different adjustments to apply to the price of financial derivatives that seem difficult to hedge. The target of this book is to make a deep review of how these effects impact the derivatives valuation theory

Application of Tensor Neural Networks to Pricing Bermudan Swaptions

The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard. In low dimensions, these approaches provide accurate and robust prices for European Swaptions but, even in this computationally simple setting, they are known to underestimate the value of Bermudan Swaptions when using the state variables as regressors. This is mainly due to the use of a finite number of predetermined basis functions in the regression. Moreover, in high-dimensional settings, these approaches succumb to the Curse of Dimensionality. To address these issues, Deep-learning techniques have been used to solve the backward Stochastic Differential Equation associated with the value process for European and Bermudan Swaptions; however, these methods are constrained by training time and memory. To overcome these limitations, we propose leveraging Tensor Neural Networks as they can provide significant parameter savings while attaining the same accuracy as classical Dense Neural Networks. In this paper we rigorously benchmark the performance of Tensor Neural Networks and Dense Neural Networks for pricing European and Bermudan Swaptions, and we show that Tensor Neural Networks can be trained faster than Dense Neural Networks and provide more accurate and robust prices than their Dense counterparts.Comment: 15 pages, 9 figures, 2 table

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition.

International audienceThe Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age-sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development. We used the published GBD 2013 data for age-specific mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specific death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantified patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time. Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6-6·6), from 65·3 years (65·0-65·6) in 1990 to 71·5 years (71·0-71·9) in 2013, HALE at birth rose by 5·4 years (4·9-5·8), from 56·9 years (54·5-59·1) to 62·3 years (59·7-64·8), total DALYs fell by 3·6% (0·3-7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6-29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non-communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age-standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specific non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the five leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional deficiencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specific estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries. Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition--in which increasing sociodemographic status brings structured change in disease burden--is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specific assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions. Bill & Melinda Gates Foundation