Evaluating the adaptive capacity of cultural landscapes to climate change: Incorporating site-specific knowledge in National Park Service vulnerability assessments

Cultural landscapes are complex systems of natural and cultural resources that are affected by changes in climatic and non-climatic factors. The National Park Service, Pacific West Region, has developed a vulnerability assessment (VA) model for identifying, evaluating, and responding to the effects of climate change to cultural landscapes by utilizing peer-reviewed data and local knowledge to inform management strategies that can reduce the vulnerability of cultural landscapes to deterioration and loss. Key to developing site-specific adaption plans is a VA based on analysis of the significance, exposure, and sensitivity of landscape characteristics and features, and identification of the management capacity to reduce the sensitivity of the cultural landscape to change. The resulting assessment compares the level of projected vulnerability of the landscape as a whole and of each characteristic or feature under evaluation, and the identification of methods for minimizing the sensitivity of the cultural landscape to climate change. This paper provides an overview of the VA model through case studies from the state of Washington, the territory of Guam, and Tinian, commonwealth of the Northern Marianas Islands

The Effects of Agricultural Extension on Farm Yields in Kenya

The paper examines effects of agricultural extension on crop yields in Kenya controlling for other determinants of yields, notably the schooling of farmers and agro-ecological characteristics of arable land. The data we use were collected by the Government of Kenya in 1982 and 1990, but the estimation results reported in the paper are based primarily on the 1982 data set. The sample used for estimation contains information about crop production, agricultural extension workers (exogenously supplied to farms), educational attainment of farmers, usage of farm inputs, among others. A quantile regression technique was used to investigate productivity effects of agricultural extension and other farm inputs over the entire conditional distribution of farm yield residuals. We find that productivity effect of agricultural extension is highest at the extreme ends of distribution of yield residuals. Complementarity of unobserved farmer ability with extension service at higher yield residuals and the diminishing returns to the extension input, which are uncompensated for by ability at the lower tail of the distribution, are hypothesized to account for this U-shaped pattern of the productivity effect of extension across yield quantiles. This finding suggests that for a given level of extension input, unobserved factors such as farm management abilities affect crop yields differently. Effects of schooling on farm yields are positive but statistically insignificant. Other determinants of farm yields that we analyze include labour input, farmer experience, agro-ecological characteristics of farms, fallow acreage, and types of crops grown.agricultural extension, economic effects

Lacunary generating functions of Hermite polynomials and symbolic methods

We employ an umbral formalism to reformulate the theory of Hermite polynomials and the derivation of the associated lacunary generating functions

The Effects of Agricultural Extension on Farm Yields in Kenya

The paper examines effects of agricultural extension on crop yields in Kenya controlling for other determinants of yields, notably the schooling of farmers and agro-ecological characteristics of arable land. The data we use were collected by the Government of Kenya in 1982 and 1990, but the estimation results reported in the paper are based primarily on the 1982 data set. The sample used for estimation contains information about crop production, agricultural extension workers (exogenously supplied to farms), educational attainment of farmers, usage of farm inputs, among others. A quantile regression technique was used to investigate productivity effects of agricultural extension and other farm inputs over the entire conditional distribution of farm yield residuals. We find that productivity effect of agricultural extension is highest at the extreme ends of distribution of yield residuals. Complementarity of unobserved farmer ability with extension service at higher yield residuals and the diminishing returns to the extension input, which are uncompensated for by ability at the lower tail of the distribution, are hypothesized to account for this U-shaped pattern of the productivity effect of extension across yield quantiles. This finding suggests that for a given level of extension input, unobserved factors such as farm management abilities affect crop yields differently. Effects of schooling on farm yields are positive but statistically insignificant. Other determinants of farm yields that we analyze include labour input, farmer experience, agro-ecological characteristics of farms, fallow acreage, and types of crops grown

City@home: Monte Carlo derivative pricing distributed on networked computers

Monte Carlo is a powerful and versatile derivative pricing tool, with the main drawback of requiring a large amount of computing time to generate enough realisations of the stochastic process. However, since realisations are independent from each other, the task is “embarrassingly” parallel and the workload can be easily distributed on a large set of processors without the need for fast networking and thus an expensive dedicated supercomputer. Such an alternative, much cheaper and more accessible way can be realised with the BOINC toolkit, distributing the Monte Carlo runs on networked clients running under Windows, Linux or various Unix variants, and recollecting the results at the end for a statistical evaluation of the price distribution at the final time. Though it is likely that the clients will belong to the intranet of a large company or institution, we gave our program the evocative name City@home in honour of the paradigmatic SETI@home project. As an application, we present the generation of synthetic high frequency financial time series for speculative option valuation in the context of uncoupled continuous-time random walks (fractional diffusion), with a Lévy marginal density function for the tick-by-tick log returns and a Mittag-Leffler marginal density function for the waiting times. Lévy deviates are generated with the Chambers-Mallows-Stuck method, Mittag-Leffler deviates with the Kozubowski-Pakes method

Ergodic transition in a simple model of the continuous double auction

We study a phenomenological model for the continuous double auction, whose aggregate order process is equivalent to two independent M/M/1 queues. The continuous double auction defines a continuous-time random walk for trade prices. The conditions for ergodicity of the auction are derived and, as a consequence, three possible regimes in the behavior of prices and logarithmic returns are observed. In the ergodic regime, prices are unstable and one can observe a heteroskedastic behavior in the logarithmic returns. On the contrary, non-ergodicity triggers stability of prices, even if two different regimes can be seen

Pricing methods for α-quantile and perpetual early exercise options based on Spitzer identities

We present new numerical schemes for pricing perpetual Bermudan and American options as well as α-quantile options. This includes a new direct calculation of the optimal exercise boundary for early-exercise options. Our approach is based on the Spitzer identities for general Lévy processes and on the Wiener–Hopf method. Our direct calculation of the price of α-quantile options combines for the first time the Dassios–Port–Wendel identity and the Spitzer identities for the extrema of processes. Our results show that the new pricing methods provide excellent error convergence with respect to computational time when implemented with a range of Lévy processes

Hilbert transform, spectral filters and option pricing

We show how spectral filters can improve the convergence of numerical schemes which use discrete Hilbert transforms based on a sinc function expansion, and thus ultimately on the fast Fourier transform. This is relevant, for example, for the computation of fluctuation identities, which give the distribution of the maximum or the minimum of a random path, or the joint distribution at maturity with the extrema staying below or above barriers. We use as examples the methods by Feng and Linetsky (Math Finance 18(3):337–384, 2008) and Fusai et al. (Eur J Oper Res 251(4):124–134, 2016) to price discretely monitored barrier options where the underlying asset price is modelled by an exponential Lévy process. Both methods show exponential convergence with respect to the number of grid points in most cases, but are limited to polynomial convergence under certain conditions. We relate these rates of convergence to the Gibbs phenomenon for Fourier transforms and achieve improved results with spectral filtering

Fluctuation identities with continuous monitoring and their application to the pricing of barrier options

We present a numerical scheme to calculate fluctuation identities for exponential Lévy processes in the continuous monitoring case. This includes the Spitzer identities for touching a single upper or lower barrier, and the more difficult case of the two-barriers exit problem. These identities are given in the Fourier-Laplace domain and require numerical inverse transforms. Thus we cover a gap in the literature that has mainly studied the discrete monitoring case; indeed, there are no existing numerical methods that deal with the continuous case. As a motivating application we price continuously monitored barrier options with the underlying asset modelled by an exponential Lévy process. We perform a detailed error analysis of the method and develop error bounds to show how the performance is limited by the truncation error of the sinc-based fast Hilbert transform used for the Wiener–Hopf factorisation. By comparing the results for our new technique with those for the discretely monitored case (which is in the Fourier-z domain) as the monitoring time step approaches zero, we show that the error convergence with continuous monitoring represents a limit for the discretely monitored scheme