Quasi-likelihood estimation for semimartingales

Hutton, James E.; Nelson, Paul I.

doi:10.1016/0304-4149(86)90004-9

Cited by 68 publications

(31 citation statements)

References 11 publications

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“…This result generalizes (1) and can be applied to the estimation of semiparametric ARCH models (Li and Turtle 2000). Finally, we recall that asymptotic consistency and normality of the resultant estimates of optimal EFs were amply discussed and proved by many authors (among others, Hutton and Nelson 1986;Godambe and Heyde 1987;Chen 1993;Heyde 1997).…”

Section: Basic Concepts Of Estimating Function Theorymentioning

confidence: 80%

Moment based approaches to value the risk of contingent claim portfolios

et al. 2008

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In this paper we describe and apply the estimating function methodology to value the risk of asset derivative portfolios. We first implement the Li's model based on the first four moments and then we show the limits of this model in forecasting the maximum loss of contingent claims. In addition, we show that four moments are not enough to describe the behavior of the lower percentiles of derivatives. Finally, we propose a model that considers the first six moments and we compare the performances of these models proposing a backtest analysis on several historical and truncated asset derivative portfolios.

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Section: Basic Concepts Of Estimating Function Theorymentioning

confidence: 80%

Moment based approaches to value the risk of contingent claim portfolios

et al. 2008

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“…MQE's can be particularly useful when maximum likelihood estimators are difficult to obtain or the likelihood is not even fully specified by the assumed model. See Hutton and Nelson (1986) for details. We will use mqe's in Section 3.2 to estimate parameters in a branching process with immigration.…”

Section: Approximate Bayes Estimatorsmentioning

confidence: 98%

“…In this case we base our approximate Bayes estimators on the mqe's given as solutions to the equations APPROXIMATE BAYES ESTIMATORS 227 Starting with xo a positive integer, the process { x & ,~ in ergodic and has a limiting stationary Poisson distribution with mean Al(1 -p). The work of Hutton and Nelson (1986) indicates that solutions to (3.4), denoted by h2(8,A), satisfy (2.1) with Kn given by We placed exponential priors on the immigration rate A with means A,, = 2kA*, k = -4 to 4 in unit steps. Motivated by a desire for simplicity, we used an independent uniform distribution on the !nit intervakas a priorAor the bouzded param-eterp.…”

Section: A Branching Process With Immigrationmentioning

confidence: 99%

Approximate bayes estimators for stochastic processes

Ogunyemi

Hutton

Nelson

1993

Journal of Statistical Computation and Simulation

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Let 8, be an estimaJor of a parameter 8 based on observing a stochastic process over a time period of length T. Suppose is asymptotically normally distributed as T + 30. Given a prior distribution on 8, we propose constructing a posterior distribution based on this asymptotic normal likelihood. Call the mean of this posterior an approximate Bayes estimator. We give conditions under which this estimator is strongly consistent and investigate its performance in reducing mean square error for a birth and death process and a branching process.

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“…Note that this estimator is equivalent to the MLE of the discretized model via the Euler approximation. The third method was proposed by Hutton and Nelson (1986). It uses mainly information in the drift function and is based on maximization of the following logarithmic quasi-likelihood function…”

Section: Examplementioning

confidence: 99%

“…The restriction on the di¤usion term regarding parameter dependence was somewhat "relaxed"in Hutton and Nelson (1986) who based estimation on the following …rst order condition of the logarithmic quasi-likelihood function:…”

Section: Approaches Based On Realized Volatility and In-…ll Likelihoodmentioning

confidence: 99%

A two-stage realized volatility approach to estimation of diffusion processes with discrete data

Phillips

2009

Journal of Econometrics

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This paper motivates and introduces a two-stage method of estimating di¤u-sion processes based on discretely sampled observations. In the …rst stage we make use of the feasible central limit theory for realized volatility, as developed in Jacod (1994) and Barndor¤-Nielsen and Shephard (2002), to provide a regression model for estimating the parameters in the di¤usion function. In the second stage the in-…ll likelihood function is derived by means of the Girsanov theorem and then used to estimate the parameters in the drift function. Consistency and asymptotic distribution theory for these estimates are established in various contexts. The …nite sample performance of the proposed method is compared with that of the approximate maximum likelihood method of Aït-Sahalia (2002).JEL Classi…cation: C13, C22, E43, G13

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Quasi-likelihood estimation for semimartingales

Cited by 68 publications

References 11 publications

Moment based approaches to value the risk of contingent claim portfolios

Moment based approaches to value the risk of contingent claim portfolios

Approximate bayes estimators for stochastic processes

A two-stage realized volatility approach to estimation of diffusion processes with discrete data

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