Model-free bounds on bilateral counterparty valuation

In the last years, counterparty default risk has experienced an increased interest both by academics as well as practitioners. This was especially motivated by the market turbulences and the financial crises over the past years which have highlighted the importance of counterparty default risk for uncollateralized derivatives. The following paper focuses on the pricing of derivatives subject to such counterparty risk. After a succinct introduction to the topic, a brief review of state-of-the-art methods for the calculation of bilateral counterparty value adjustments is presented. Due to some weaknesses of these models, a novel method for the determination of model-free tight lower and upper bounds on these adjustments is presented. It will be shown in detail how these bounds can be easily and eciently calculated by the solution of a corresponding linear optimization problem. It will be illustrated how usual discretization methods like Monte Carlo methods allow to reduce the calculation of bounds to an ordinary finite dimensional transportation problem, whereas a continuous time approach will lead to a general mass transportation problem. The paper is closed with several applications of these model-free bounds, like stress-testing and estimation of model reserves.Counterparty risk, CVA, model risk

Model-free bounds on bilateral counterparty valuation

In the last years, counterparty default risk has experienced an increased interest both by academics as well as practitioners. This was especially motivated by the market turbulences and the financial crises over the past years which have highlighted the importance of counterparty default risk for uncollateralized derivatives. The following paper focuses on the pricing of derivatives subject to such counterparty risk. After a succinct introduction to the topic, a brief review of state-of-the-art methods for the calculation of bilateral counterparty value adjustments is presented. Due to some weaknesses of these models, a novel method for the determination of model-free tight lower and upper bounds on these adjustments is presented. It will be shown in detail how these bounds can be easily and eciently calculated by the solution of a corresponding linear optimization problem. It will be illustrated how usual discretization methods like Monte Carlo methods allow to reduce the calculation of bounds to an ordinary finite dimensional transportation problem, whereas a continuous time approach will lead to a general mass transportation problem. The paper is closed with several applications of these model-free bounds, like stress-testing and estimation of model reserves

In pursuit of delay-related brain activity for anticipatory eye movements

How the brain stores motion information and subsequently uses it to follow a moving target is largely unknown. This is mainly due to previous fMRI studies using paradigms in which the eye movements cannot be segregated from the storage of this motion information. To avoid this problem we used a novel paradigm designed in our lab in which we interlaced a delay (2, 4 or 6 seconds) between the 1st and 2nd presentation of a moving stimulus. Using this design we could examine brain activity during a delay period using fMRI and have subsequently found a number of brain areas that reveal sustained activity during predictive pursuit. These areas include, the V5 complex and superior parietal lobe. This study provides new evidence for the network involved in the storage of visual information to generate early motor responses in pursuit

Influenza transmission dynamics quantified from RNA in wastewater in Switzerland

INTRODUCTION: Influenza infections are challenging to monitor at the population level due to many mild and asymptomatic cases and similar symptoms to other common circulating respiratory diseases, including COVID-19. Methods for tracking cases outside of typical reporting infrastructure could improve monitoring of influenza transmission dynamics. Influenza shedding into wastewater represents a promising source of information where quantification is unbiased by testing or treatment-seeking behaviours. METHODS: We quantified influenza A and B virus loads from influent at Switzerland’s three largest wastewater treatment plants, serving about 14% of the Swiss population (1.2 million individuals). We estimated trends in infection incidence and the effective reproductive number (Re) in these catchments during a 2021/22 epidemic and compared our estimates to typical influenza surveillance data. RESULTS: Wastewater data captured the same overall trends in infection incidence as laboratory-confirmed case data at the catchment level. However, the wastewater data were more sensitive in capturing a transient peak in incidence in December 2021 than the case data. The Re estimated from the wastewater data was roughly at or below the epidemic threshold of 1 during work-from-home measures in December 2021 but increased to at or above the epidemic threshold in two of the three catchments after the relaxation of these measures. The third catchment yielded qualitatively the same results but with wider confidence intervals. The confirmed case data at the catchment level yielded comparatively less precise R_e estimates before and during the work-from-home period, with confidence intervals that included one before and during the work-from-home period. DISCUSSION: Overall, we show that influenza RNA in wastewater can help monitor nationwide influenza transmission dynamics. Based on this research, we developed an online dashboard for ongoing wastewater-based influenza surveillance in Switzerland.ISSN:1424-7860ISSN:1424-399