Future pricing through homogeneous semi-Markov processes
Copyright policy )Future pricing through homogeneous semi-Markov processes
A model for future currency pricing is considered based on a discrete time homogeneous semi-Markov process with finite state space. It is applied to the data on Italian lire from 27 March 98 to 17 September 98. The values of raw time series are discretized to obtain 122 states of the process. Relative frequencies are used for estimation of the transition probabilities in the embedded Markov chain and empirical CDFs for estimation of the waiting time distributions. The total number of parameters in the obtained model is 133956. The authors propose to use the obtained predictive distributions for prediction of the mean value of future pricing and VaR evaluation.
Applications of statistics to actuarial sciences and financial mathematics, price expectation on the future contracts, Applications of Markov renewal processes (reliability, queueing networks, etc.), Markov processes: estimation; hidden Markov models, prediction, Fib30, Finance etc., applications to actuarial sciences; fib30; financial models; future pricing; price expectation of the future contracts; semi-markov processes; stochastic processes
Applications of statistics to actuarial sciences and financial mathematics, price expectation on the future contracts, Applications of Markov renewal processes (reliability, queueing networks, etc.), Markov processes: estimation; hidden Markov models, prediction, Fib30, Finance etc., applications to actuarial sciences; fib30; financial models; future pricing; price expectation of the future contracts; semi-markov processes; stochastic processes
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!