A volatility decomposition control variate technique for Monte Carlo simulations of Heath Jarrow Morton models

The aim of this work is to develop a simulation approach to the yield curve evolution in the Heath, Jarrow and Morton [Econometrica 60 (1) (1992) 77] framework. The stochastic quantities considered as affecting the forward rate volatility function are the spot rate and the forward rate. A decomposition of the volatility function into a Hull and White [Rev. Financial Stud. 3 (1990) 573] volatility and a remainder allows us to develop an efficient Control Variate Method that makes use of the closed form solution of the Hull and White call option. This technique considerably speeds up the simulation algorithm to approximate call option values with Monte Carlo simulation. © 2003 Elsevier B.V. All rights reserved

Entropy of the Nordic electricity market: anomalous scaling, spikes, and mean-reversion

The electricity market is a very peculiar market due to the large variety of phenomena that can affect the spot price. However, this market still shows many typical features of other speculative (commodity) markets like, for instance, data clustering and mean reversion. We apply the diffusion entropy analysis (DEA) to the Nordic spot electricity market (Nord Pool). We study the waiting time statistics between consecutive spot price spikes and find it to show anomalous scaling characterized by a decaying power-law. The exponent observed in data follows a quite robust relationship with the one implied by the DEA analysis. We also in terms of the DEA revisit topics like clustering, mean-reversion and periodicities. We finally propose a GARCH inspired model but for the price itself. Models in the context of stochastic volatility processes appear under this scope to have a feasible description.Comment: 16 pages, 7 figure

Optimal quantization for the pricing of swing options

In this paper, we investigate a numerical algorithm for the pricing of swing options, relying on the so-called optimal quantization method. The numerical procedure is described in details and numerous simulations are provided to assert its efficiency. In particular, we carry out a comparison with the Longstaff-Schwartz algorithm.Comment: 27

Pricing Exotic Options in a Path Integral Approach

In the framework of Black-Scholes-Merton model of financial derivatives, a path integral approach to option pricing is presented. A general formula to price European path dependent options on multidimensional assets is obtained and implemented by means of various flexible and efficient algorithms. As an example, we detail the cases of Asian, barrier knock out, reverse cliquet and basket call options, evaluating prices and Greeks. The numerical results are compared with those obtained with other procedures used in quantitative finance and found to be in good agreement. In particular, when pricing at-the-money and out-of-the-money options, the path integral approach exhibits competitive performances.Comment: 21 pages, LaTeX, 3 figures, 6 table

Jumps and stochastic volatility in crude oil prices and advances in average option pricing

Crude oil derivatives form an important part of the global derivatives market. In this paper, we focus on Asian options which are favoured by risk managers being effective and cost-saving hedging instruments. The paper has both empirical and theoretical contributions: we conduct an empirical analysis of the crude oil price dynamics and develop an accurate pricing setup for arithmetic Asian options with discrete and continuous monitoring featuring stochastic volatility and discontinuous underlying asset price movements. Our theoretical contribution is applicable to various commodities exhibiting similar stylized properties. We here estimate the stochastic volatility model with price jumps as well as the nested model with omitted jumps to NYMEX WTI futures vanilla options. We find that price jumps and stochastic volatility are necessary to fit options. Despite the averaging effect, we show that Asian options remain sensitive to jump risk and that ignoring the discontinuities can lead to substantial mispricings

Niche partitioning of sympatric penguins by leapfrog foraging is resilient to climate change

1.Interspecific competition can drive niche partitioning along multidimensional axes, including allochrony. Competitor matching will arise where the phenology of sympatric species with similar ecological requirements respond to climate change at different rates such that allochrony is reduced. 2.Our study quantifies the degree of niche segregation in foraging areas and depths that arises from allochrony in sympatric Adélie and chinstrap penguins and explores its resilience to climate change. 3.Three‐dimensional tracking data were sampled during all stages of the breeding season and were used to parameterise a behaviour‐based model that quantified spatial overlap of foraging areas under different scenarios of allochrony. 4.The foraging ranges of the two species were similar within breeding stages, but differences in their foraging ranges between stages, combined with the observed allochrony of 28 days, resulted in them leapfrogging each other through the breeding season such that they were exploiting different foraging locations on the same calendar dates. Allochrony reduced spatial overlap in the peripheral utilisation distribution of the two species by 54.0% over the entire breeding season, compared to a scenario where the two species bred synchronously. 5.Analysis of long‐term phenology data revealed that both species advanced their laying dates in relation to October air temperatures at the same rate, preserving allochrony and niche partitioning. However if allochrony is reduced by just a single day, the spatial overlap of the core utilisation distribution increased by an average of 2.1% over the entire breeding season. 6.Niche partitioning between the two species by allochrony appears to be resilient to climate change and so competitor matching cannot be implicated in the observed population declines of the two penguin species across the Western Antarctic Peninsula

Environmental DNA reveals links between abundance and composition of airborne grass pollen and respiratory health

This is the final version. Available on open access from Elsevier via the DOI in this recordData and Code Availability Statement: Data collected using qPCR is archived and on NERC EIDC [https://doi.org/10.5285/28208be4-0163-45e6-912c-2db205126925]. Standard pollen monitoring ‘count’ data were sourced from the MEDMI database, with the exception of data from Bangor which were produced as part of the present study and are available on request. Prescribing datasets are publicly available, as are weather, air pollution, deprivation (IMD) and rural-urban category data. Hospital episode statistics (HES) datasets are sensitive, individual-level health data, which are subject to strict privacy regulations and are not publicly available. The study did not generate any unique codeGrass (Poaceae) pollen is the most important outdoor aeroallergen, exacerbating a range of respiratory conditions, including allergic asthma and rhinitis (‘hay fever’). Understanding the relationships between respiratory diseases and airborne grass pollen with view to improving forecasting has broad public health and socioeconomic relevance. It is estimated that there are over 400 million people with allergic rhinitis and over 300 million with asthma, globally, often comorbidly . In the UK, allergic asthma has an annual cost of around US$ 2.8 billion (2017). The relative contributions of the >11,000 (worldwide) grass species to respiratory health have been unresolved, as grass pollen cannot be readily discriminated using standard microscopy. Instead, here we used novel environmental DNA (eDNA) sampling and quantitative PCR (qPCR) , to measure the relative abundances of airborne pollen from common grass species, during two grass pollen seasons (2016 and 2017), across the UK. We quantitatively demonstrate discrete spatiotemporal patterns in airborne grass pollen assemblages. Using a series of generalised additive models (GAMs), we explore the relationship between the incidences of airborne pollen and severe asthma exacerbations (sub-weekly) and prescribing rates of drugs for respiratory allergies (monthly). Our results indicate that a subset of grass species may have disproportionate influence on these population-scale respiratory health responses during peak grass pollen concentrations. The work demonstrates the need for sensitive and detailed biomonitoring of harmful aeroallergens in order to investigate and mitigate their impacts on human health.Natural Environment Research Council (NERC)National Institute for Health Research (NIHR)Public Health EnglandUniversity of ExeterUniversity College LondonMet Offic

Predicting the severity of the grass pollen season and the effect of climate change in Northwest Europe

Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season's severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season