1,676 research outputs found
Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models
The purpose of this paper is to improve the accuracy of dynamic hedging using
implied volatilities generated by genetic programming. Using real data from
S&P500 index options, the genetic programming's ability to forecast Black and
Scholes implied volatility is compared between static and dynamic
training-subset selection methods. The performance of the best generated GP
implied volatilities is tested in dynamic hedging and compared with
Black-Scholes model. Based on MSE total, the dynamic training of GP yields
better results than those obtained from static training with fixed samples.
According to hedging errors, the GP model is more accurate almost in all
hedging strategies than the BS model, particularly for in-the-money call
options and at-the-money put options.Comment: 32 pages,13 figures, Intech Open Scienc
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
Effect of reservoir zones and hedging factor dynamism on reservoir adaptive capacity for climate change impacts
When based on the zones of available water in storage, hedging has
traditionally used a single hedged zone and a constant rationing ratio for
constraining supply during droughts. Given the usual seasonality of
reservoir inflows, it is also possible that hedging could feature multiple
hedged zones and temporally varying rationing ratios but very few studies
addressing this have been reported especially in relation to adaptation to
projected climate change. This study developed and tested Genetic Algorithms
(GA) optimised zone-based operating policies of various configurations using
data for the Pong reservoir, Himachal Pradesh, India. The results show that
hedging does lessen vulnerability, which dropped from ≥ 60 % without
hedging to below 25 % with the single stage hedging. More complex hedging
policies, e.g. two stage and/or temporally varying rationing ratios only
produced marginal improvements in performance. All this shows that water
hedging policies do not have to be overly complex to effectively offset
reservoir vulnerability caused by water shortage resulting from e.g.
projected climate change
INDUSTRIALIZATION OF DERIVATIVE DESIGN: INTEGRATED RISK MANAGEMENT WITH THE FINANCIAL INFORMATION SYSTEM WARRANT-PRO-2
Risk management is essential in a modern financial services industry. Derivative instruments like options have a particular status. Appropriate derivatives allow financial service providers to redistribute risks towards others. The process of creating customer tailored derivatives is not wellinvestigated today. With the financial information system (FIS) WARRANT-PRO-2 derivative prices are computed for given payments. The deviation, for example, from a predefinable Delta of an option can be minimized. Automatic creation of optimally synthesized options is very promising for buyer and seller. An example is presented to show the easy process of creating a customer tailored option
Enhancing Farm Profitability through Portfolio Analysis: The Case of Spatial Rice Variety Selection.
The objectives of this paper is to use the large depth of existing literature on portfolio theory and apply it to rice varietal selection for 6 counties in the Arkansas Delta. Results based on 1999-2006 data suggests that combining available varieties using portfolio theory could have increased profits from 3 to 26% (dependent on location) in the Arkansas Delta. The major implication of this research is that data and statistical tools are available to improve the choice of rice varieties to plant each year in specific locations within Arkansas. Specifically, there are large potential gains from combining varieties that are characterized by inverse yield responses to growing conditions such as drought, pest infestation, or the presence of a specific disease.Rice, portfolio analysis, optimal variety selection, risk analysis., Production Economics, D81, Q16, Q12,
A survey on metaheuristics for stochastic combinatorial optimization
Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel
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