Abstract The sweetpotato whitefly Bemisia tabaci is a highly destructive agricultural and ornamental crop pest. It damages host plants through both phloem feeding and vectoring plant pathogens. Introductions of B. tabaci are difficult to quarantine and eradicate because of its high reproductive rates, broad host plant range, and insecticide resistance. A total of 791 Gb of raw DNA sequence from whole genome shotgun sequencing, and 13 BAC pooling libraries were generated by Illumina sequencing using different combinations of mate-pair and pair-end libraries. Assembly gave a final genome with a scaffold N50 of 437 kb, and a total length of 658 Mb. Annotation of repetitive elements and coding regions resulted in 265.0 Mb TEs (40.3%) and 20 786 protein-coding genes with putative gene family expansions, respectively. Phylogenetic analysis based on orthologs across 14 arthropod taxa suggested that MED/Q is clustered into a hemipteran clade containing A. pisum and is a sister lineage to a clade containing both R. prolixus and N. lugens. Genome completeness, as estimated using the CEGMA and Benchmarking Universal Single-Copy Orthologs pipelines, reached 96% and 79%. These MED/Q genomic resources lay a foundation for future ‘pan-genomic’ comparisons of invasive vs. noninvasive, invasive vs. invasive, and native vs. exotic Bemisia, which, in return, will open up new avenues of investigation into whitefly biology, evolution, and management.
A precision measurement by AMS of the antiproton flux and the antiproton-to-proton flux ratio in primary cosmic rays in the absolute rigidity range from 1 to 450 GV is presented based on 3.49×10^{5} antiproton events and 2.42×10^{9} proton events. The fluxes and flux ratios of charged elementary particles in cosmic rays are also presented. In the absolute rigidity range ∼60 to ∼500 GV, the antiproton p[over ¯], proton p, and positron e^{+} fluxes are found to have nearly identical rigidity dependence and the electron e^{-} flux exhibits a different rigidity dependence. Below 60 GV, the (p[over ¯]/p), (p[over ¯]/e^{+}), and (p/e^{+}) flux ratios each reaches a maximum. From ∼60 to ∼500 GV, the (p[over ¯]/p), (p[over ¯]/e^{+}), and (p/e^{+}) flux ratios show no rigidity dependence. These are new observations of the properties of elementary particles in the cosmos.
We extend the Q-learner in Black-Scholes (QLBS) framework by incorporating risk aversion and trading costs, and propose a novel Replication Learning of Option Pricing (RLOP) approach. Both methods are fully compatible with standard reinforcement learning algorithms and operate under market frictions. Using SPY and XOP option data, we evaluate performance along static and dynamic dimensions. Adaptive-QLBS achieves higher static pricing accuracy in implied volatility space, while RLOP delivers superior dynamic hedging performance by reducing shortfall probability. These results highlight the importance of evaluating option pricing models beyond static fit, emphasizing realized hedging outcomes.
A new semi-analytical pricing model for Bermudan swaptions based on swap rates distributions and correlations between them. The model does not require product specific calibration.
We derive the short-maturity asymptotics for option prices in the local volatility model in a new short-maturity limit $T\to 0$ at fixed $ρ= (r-q) T$, where $r$ is the interest rate and $q$ is the dividend yield. In cases of practical relevance $ρ$ is small, however our result holds for any fixed $ρ$. The result is a generalization of the Berestycki-Busca-Florent formula for the short-maturity asymptotics of the implied volatility which includes interest rates and dividend yield effects of $O(((r-q) T)^n)$ to all orders in $n$. We obtain analytical results for the ATM volatility and skew in this asymptotic limit. Explicit results are derived for the CEV model. The asymptotic result is tested numerically against exact evaluation in the square-root model model $σ(S)=σ/\sqrt{S}$, which demonstrates that the new asymptotic result is in very good agreement with exact evaluation in a wide range of model parameters relevant for practical applications.
We develop two alternate approaches to arbitrage-free, market-complete, option pricing. The first approach requires no riskless asset. We develop the general framework for this approach and illustrate it with two specific examples. The second approach does use a riskless asset. However, by ensuring equality between real-world and risk-neutral price-change probabilities, the second approach enables the computation of risk-neutral option prices utilizing expectations under the natural world probability P. This produces the same option prices as the classical approach in which prices are computed under the risk neutral measure Q. The second approach and the two specific examples of the first approach require the introduction of new, marketable asset types, specifically perpetual derivatives of a stock, and a stock whose cumulative return (rather than price) is deflated.
We report high statistics measurements of inclusive charged hadron production in Au+Au and p+p collisions at sqrt[s(NN)]=200 GeV. A large, approximately constant hadron suppression is observed in central Au+Au collisions for 5<p(T)<12 GeV/c. The collision energy dependence of the yields and the centrality and p(T) dependence of the suppression provide stringent constraints on theoretical models of suppression. Models incorporating initial-state gluon saturation or partonic energy loss in dense matter are largely consistent with observations. We observe no evidence of p(T)-dependent suppression, which may be expected from models incorporating jet attenuation in cold nuclear matter or scattering of fragmentation hadrons.
AMS-02 is wide acceptance high-energy physics experiment installed on the International Space Station in May 2011 and operating continuously since then. AMS-02 is able to precisely separate cosmic rays light nuclei (1≤ Z ≤ 8) with contaminations less than 10−3. The light nuclei cosmic ray Boron to Carbon flux ratio is very well known sensitive observable for the understanding of the propagation of cosmic rays in the Galaxy, being Boron a secondary product of spallation on the interstellar medium of heavier primary elements such as Carbon and Oxygen. A precision measurement based on 10 million events of the Boron to Carbon ratio in the rigidity range from 2 GV to 1.8 TV is presented.
Sebastian Jaimungal, Silvana M. Pesenti, Leandro Sánchez-Betancourt
Given an n-dimensional stochastic process X driven by P-Brownian motions and Poisson random measures, we seek the probability measure Q, with minimal relative entropy to P, such that the Q-expectations of some terminal and running costs are constrained. We prove existence and uniqueness of the optimal probability measure, derive the explicit form of the measure change, and characterise the optimal drift and compensator adjustments under the optimal measure. We provide an analytical solution for Value-at-Risk (quantile) constraints, discuss how to perturb a Brownian motion to have arbitrary variance, and show that pinned measures arise as a limiting case of optimal measures. The results are illustrated in a risk management setting -- including an algorithm to simulate under the optimal measure -- where an agent seeks to answer the question: what dynamics are induced by a perturbation of the Value-at-Risk and the average time spent below a barrier on the reference process?
Exact relationships between the short time-to-maturity ATM implied volatility slope, the (dual) volatility swap, and the (dual) zero vanna implied volatility are given.
In this paper, we provide a unified treatment of the Vanna-Volga pricing technique. We derive the value of single and double barriers FX options, as well as closed formulas for the Delta, Vega, Vanna and Volga of those contracts.
The construction of approximate replication strategies for pricing and hedging of derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for hedging under realistic market conditions have attracted significant interest. While research in the derivatives area mostly focused on variations of $Q$-learning, in artificial intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem behind the pricing and hedging tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework.
This paper considers an insurance surplus process modeled by a spectrally negative Lévy process. Instead of the time of ruin in the traditional setting, we apply the time of drawdown as the risk indicator in this paper. We study the joint distribution of the time of drawdown, the running maximum at drawdown, the last minimum before drawdown, the surplus before drawdown and the surplus at drawdown (may not be deficit in this case), which generalizes the known results on the classical expected discounted penalty function in Gerber and Shiu (1998). The results have semi-explicit expressions in terms of the $q$-scale functions and the Lévy measure associated with the Lévy process. As applications, the obtained result is applied to recover results in the literature and to obtain new results for the Gerber-Shiu function at ruin for risk processes embedded with a loss-carry-forward taxation system or a barrier dividend strategy. Moreover, numerical examples are provided to illustrate the results.