Sharp Analytical Lower Bounds for the Price of a Convertible Bond

Analytical approximations for the price of a convertible bond within defaultable Markov diffusion models are derived in this article. Because convertible bond pricing requires time-consuming finite difference or tree pricing methods in general, such proxy formulas can help to calibrate model parameters more efficiently. The derivation is based on the idea of “Europeanizing” the American conversion option of the holder. Hence, the quality of the approximations stands and falls with the value of the early conversion premium. In practice, the latter is typically close to zero, which implies that the analytical lower bounds are incredibly sharp.

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.


2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
C. F. Lo

The Lie-algebraic approach has been applied to solve the bond pricing problem in single-factor interest rate models. Four of the popular single-factor models, namely, the Vasicek model, Cox-Ingersoll-Ross model, double square-root model, and Ahn-Gao model, are investigated. By exploiting the dynamical symmetry of their bond pricing equations, analytical closed-form pricing formulae can be derived in a straightfoward manner. Time-varying model parameters could also be incorporated into the derivation of the bond price formulae, and this has the added advantage of allowing yield curves to be fitted. Furthermore, the Lie-algebraic approach can be easily extended to formulate new analytically tractable single-factor interest rate models.


Author(s):  
Charchit Kumar ◽  
Alejandro Palacios ◽  
Venkata A. Surapaneni ◽  
Georg Bold ◽  
Marc Thielen ◽  
...  

The surfaces of animals, plants and abiotic structures are not only important for organismal survival, but they have also inspired countless biomimetic and industrial applications. Additionally, the surfaces of animals and plants exhibit an unprecedented level of diversity, and animals often move on the surface of plants. Replicating these surfaces offers a number of advantages, such as preserving a surface that is likely to degrade over time, controlling for non-structural aspects of surfaces, such as compliance and chemistry, and being able to produce large areas of a small surface. In this paper, we compare three replication techniques among a number of species of plants, a technical surface and a rock. We then use two model parameters (cross-covariance function ratio and relative topography difference) to develop a unique method for quantitatively evaluating the quality of the replication. Finally, we outline future directions that can employ highly accurate surface replications, including ecological and evolutionary studies, biomechanical experiments, industrial applications and improving haptic properties of bioinspired surfaces. The recent advances associated with surface replication and imaging technology have formed a foundation on which to incorporate surface information into biological sciences and to improve industrial and biomimetic applications. This article is part of the theme issue ‘Bioinspired materials and surfaces for green science and technology’.


2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]


2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.


1999 ◽  
Vol 39 (8) ◽  
pp. 55-62 ◽  
Author(s):  
Anastasios I. Stamou ◽  
Bogdana Koumanova ◽  
Stoyan Stoyanov ◽  
Georgy Atanasov ◽  
Konstantinos Pipilis

A general methodology for the study of water quality in rivers is presented. The paper consists of four parts. In the first part the general characteristics of the area of study, which is the Beli Lom river, and its major pollution sources are presented. The effluent of the Razgrad Wastewater Treatment Plant (RWWTP) has been identified as the most significant pollution point source, due to the inadequate performance of the plant. The second part deals with data collection and processing. Four series of data have been collected, including physical, flow and water quality characteristics. In the third part a 1-d, finite-difference, second-order model is presented. In the fourth part, the model is calibrated, for the determination of its main coefficients, and is successfully verified by predicting the BOD and DO concentrations in the Beli Lom river for all series of data. Finally, the model has been applied to determine the maximum BOD and minimum DO effluent concentrations of the RWWTP, so that a minimum DO concentration is maintained throughout the river.


2021 ◽  
Vol 314 ◽  
pp. 05002
Author(s):  
Hasna Moumni ◽  
Karima Sebari ◽  
Laila Stour ◽  
Abdellatif Ahbari

The availability, accessibility and quality of data are significant obstacles to hydrological modelling. Estimating the initial values of the hydrological model´’ ’s parameters is a laborious and determining task requiring much attention. Geographic information systems (GIS) and spatial remote sensing are prometting tools for processing and collecting data. In this work, we use an innovative approach to estimate the HEC-HMS hydrological model parameters from the soil map of Africa (250m), the land use map GLC30, the depth to bedrock map, the digital elevation model and observed flow data. The estimation approach is applied to the Ouergha basin (Sebou, Morocco). The proposed approach’s interest is to feed the HEC-HMS hydrological model with initial values of parameters close to the study area reality instead of using random parameters.


2020 ◽  
Author(s):  
Catherine Manning ◽  
Eric-Jan Wagenmakers ◽  
Anthony Norcia ◽  
Gaia Scerif ◽  
Udo Boehm

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.


Author(s):  
Kevin H. Knuth ◽  
Robert M. Powell ◽  
Peter A. Reali

A number of Unidentified Aerial Phenomena (UAP) encountered by military, commercial, and civilian aircraft have been reported to be structured craft that exhibit 'impossible' flight characteristics. We consider a handful of well-documented encounters, including the 2004 encounters with the Nimitz Carrier Group off the coast of California, and estimate lower bounds on the accelerations exhibited by the craft during the observed maneuvers. Estimated accelerations range from almost 100g to 1000s of g's with no observed air disturbance, no sonic booms, and no evidence of excessive heat commensurate with even the minimal estimated energies. In accordance with observations, the estimated parameters describing the behavior of these craft are both anomalous and surprising. The extreme estimated flight characteristics reveal that these observations are either fabricated or seriously in error, or that these craft exhibit technology far more advanced than any known craft on Earth. In many cases, the number and quality of witnesses, the variety of roles they played in the encounters, and the equipment used to track and record the craft favor the latter hypothesis that these are indeed technologically advanced craft. The observed flight characteristics of these craft are consistent with the flight characteristics required for interstellar travel. That is, if these observed accelerations were sustainable in space, then these craft could easily reach relativistic speeds within a matter of minutes to hours and cover interstellar distances in a matter of days to weeks, proper time.


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