The multi-period binomial model
ثبت نشده
چکیده
The present time, denoted at n = 0 and the expiration time, denoted at n = N . The lower case letter n, k (and occasionally i, j,m) will be used to denote the time variable. The notation Sk (or Sn, Si, Sj · · · ) denotes the value of the stock (the underlying) at time k (or time n, i, j · · · ). An important convention we’ll use is that S0 will always be a constant, that is the present value of the stock is always known. For any k ≥ 1, Sk is a random variable. The specific distribution of Sk will be discussed below. Similarly, we’ll denote Vk to be the value of a specific financial product at time k. In particular, if V is the European call option with strike K and expiration N , then VN = (SN −K). We’ll also denote πk to be the value of a specific portfolio at time k. In particular, if the portfolio is replicating then VN = πN . Also note that V0, π0 are also constants, and for k ≥ 1, Vk, πk are random variables. We will suppose that the time intervals between any two discrete moments k, k+1 are the same, denoted as ∆T . Thus the expiration time can also be written as T = N∆T . For a replicating portfolio, we will denote the number of shares of S we hold at a particular time as ∆k (do not confuse this with the interval length ∆T . In general, ∆k will also be a random variable (which is easy to understand, as the number of shares we hold at time k will depend on the actual value of Sk at that time). The interest rate will be denoted as r.
منابع مشابه
The Discrete Binomial Model for Option Pricing
This paper introduces the notion of option pricing in the context of financial markets. The discrete time, one-period binomial model is explored and generalized to the multi-period binomial model. The multi-period model is then redeveloped using the sophisticated tools of martingale theory. The paper concludes with a brief extension of the results to continuous time, giving a heuristic derivati...
متن کاملBeta - Binomial and Ordinal Joint Model with Random Effects for Analyzing Mixed Longitudinal Responses
The analysis of discrete mixed responses is an important statistical issue in various sciences. Ordinal and overdispersed binomial variables are discrete. Overdispersed binomial data are a sum of correlated Bernoulli experiments with equal success probabilities. In this paper, a joint model with random effects is proposed for analyzing mixed overdispersed binomial and ordinal longitudinal respo...
متن کاملModeling the Number of Attacks in Multiple Sclerosis Patients Using Zero-Inflated Negative Binomial Model
Background and aims: Multiple sclerosis (MS) is an inflammatory disease of the central nervous system.The impact of the number of attacks on the disease is undeniable. The aim of this study was to analyze thenumber of attacks in these patients.Methods: In this descriptive-analytical study, the registered data of 1840 MS patients referred to the MS clinicof Ayatollah Kash...
متن کاملEstimation of Count Data using Bivariate Negative Binomial Regression Models
Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
متن کاملua nt - p h / 01 12 15 8 v 2 4 J ul 2 00 2 Quantum Finance : The Finite Dimensional Case ∗
In this paper, we present a quantum version of some portions of Mathematical Finance, including theory of arbitrage, asset pricing, and optional decomposition in financial markets based on finite dimensional quantum probability spaces. As examples, the quantum model of binomial markets is studied. We show that this quantum model ceases to pose the paradox which appears in the classical model of...
متن کاملIdentifying the change time of multivariate binomial processes for step changes and drifts
In this paper, a new control chart to monitor multi-binomial processes is first proposed based on a transformation method. Then, the maximum likelihood estimators of change points designed for both step changes and linear-trend disturbances are derived. At the end, the performances of the proposed change-point estimators are evaluated and are compared using some Monte Carlo simulation experimen...
متن کامل