Discrete Dynamics in Nature and Society

Volume 2018, Article ID 9549707, 8 pages

https://doi.org/10.1155/2018/9549707

## Estimation for a Second-Order Jump Diffusion Model from Discrete Observations: Application to Stock Market Returns

^{1}School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China^{2}School of Science, Nanjing Audit University, Nanjing, Jiangsu, China^{3}School of Science, Xi’an Polytechnic University, Xi’an, Shaanxi, China

Correspondence should be addressed to Tianshun Yan; moc.361@nay_nuhsnait

Received 2 February 2018; Accepted 24 May 2018; Published 13 June 2018

Academic Editor: Xiaohua Ding

Copyright © 2018 Tianshun Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper proposes a second-order jump diffusion model to study the jump dynamics of stock market returns via adding a jump term to traditional diffusion model. We develop an appropriate maximum likelihood approach to estimate model parameters. A simulation study is conducted to evaluate the performance of the estimation method in finite samples. Furthermore, we consider a likelihood ratio test to identify the statistically significant presence of jump factor. The empirical analysis of stock market data from North America, Asia, and Europe is provided for illustration.

#### 1. Introduction

Continuous-time stochastic processes have been widely used to model securities prices for option valuation. Nicolau considered a second-order diffusion process which is defined bywhere and are the drift and diffusion functions, respectively [1]. is a standard one-dimensional Brownian motion. is directly observable and differentiable. In this model, can also be expressed as the integrated processFor model (1), a nonparametric approach which is based on the infinitesimal generator and Taylor series expansion has been developed to estimate the drift and diffusion functions [1]. Thereafter, Wang and Lin [2] presented the local linear estimation of the diffusion and drift functions and proved that the estimators are weakly consistent. Wang et al. [3] proposed the reweighted estimation of the diffusion function and investigated the consistency of the estimator. Furthermore, Hanif [4] studied the nonparametric estimation of the drift and diffusion functions using an asymmetric kernel and proved that the estimators are consistent and asymptotically normal.

As pointed out by Nicolau [1, 5], model (1) is especially useful in empirical finance. First, the model accommodates nonstationary integrated stochastic process that can be made stationary by differencing. Second, in the context of stock prices, represents stock return and indicates the cumulation of . The model suggests directly modeling return rather than stock price and meets many general properties of stock returns such as stationarity in the mean, nonnormality of the distribution and weak autocorrelation.

In financial markets, the correct specification of drift and volatility is essential and instructive among practitioners in obtaining valid conclusions. Unfortunately, the existing economic theory generally provides little guidance about the precise specification of them. Model misspecification may lead to misleading conclusions in estimation and hypothesis testing. Therefore, much attention has been paid to the issue of specifying the functions forms for continuous-time diffusion models. On the other hand, it has the advantage that the problem of its estimation can be reduced to the determination of some low-dimensional parameters by applying more efficient statistical methods (see [6, 7] for details). In particular, Nicolau [1, 5] pointed out thatis a promising model for stock returns and possesses some interesting properties. In Nicolau’s empirical studies on the American stock markets, a regular pattern in all estimators of drift and diffusion can be observed: the drift is clearly linear, the volatility is a quadratic function with a minimum in the neighborhood of zero, and the specification fits the nonparametric estimators very well. Furthermore, Yan and Mei [8] developed the generalized likelihood ratio test to check the empirical finding of Nicolau. The empirical analysis of real-world data sets demonstrates that it is in general reasonable to suppose that the volatility is a quadratic function in stock markets.

The empirical distribution of stock returns typically exhibits skewness and excess kurtosis, which could be induced by various macroeconomic shocks, such as the unemployment announcement, the Gulf war, and the oil crisis. Unfortunately, the standard second-order diffusion framework (3) is not tailored to capture these stylized facts. A more appropriate specification is to modify the aforementioned standard second-order diffusion model to allow for discontinuity. This is easily obtained by combining the general diffusion model with a jump factor. The popularity of the jump diffusion models stems from at least two facts. First, as distinguished from pure diffusion processes, the jump processes can affect and match high levels of kurtosis and skewness. Second, they are economically attractive because they admit that stock prices change by sudden jumps in a short time, which is a reasonable assumption for an efficient stock market. Recently, jump noises have been also applied in population models to describe the abrupt changes of population sizes (see [9, 10], etc.). For example, Zhou et al. [9] introduced a two-population mortality model with transitory jump effects and applied it to pricing catastrophic mortality securitizations.

In view of the abovementioned facts, we extend the work of Nicolau [1, 5] and then concentrate on a new second-order jump diffusion model:where the arrival of jumps is governed by the continuous-time Poisson process with frequency parameter , which denotes the average number of jumps per year. The jump size may be a constant or may be drawn from a probability distribution. The diffusion and Poisson process are independent of each other, and each of them is independent of jump as well. Consequently, the stock return is the sum of three components. The component represents the instantaneous expected return on the stock. The part describes the instantaneous variance of the stock return due to the arrival of “normal” information, and the part describes the total instantaneous stock return owing to the arrival of “abnormal” information. Next, we develop parameter estimation methodology to estimate the coefficients in the drift, diffusion, and jump factor.

#### 2. Model Estimation

For model (4), the integrated process is generally observable at the time points , while is a nonobservable process. In fact, for the fixed sampling interval , the exact distribution of is generally not explicit. An exception is the case where follows an Ornstein-Uhlenbeck process [11]. In practical applications, the time points are equally spaced. For example, when the time unit is a year, weekly data are sampled at for a given initial time point . Based on and the discrete-time observations , we haveThen can be approximated byNaturally, the smaller the time span is, the closer is to . In fact, stock prices are usually observed daily or higher frequency.

Let and = According to the independent increment property of the standard Brownian motion, are independently and normally distributed with mean zero and variance . Therefore, we can write as , where are independently and identically distributed as the standard normal distribution. Then the Euler discretization of model (4) can be expressed as

where is normally distributed with mean and variance . is the discrete-time Poisson increment. We set the mean jump size equal to zero, = 0, guaranteeing a symmetric stock return distribution according to [12, 13]. It is well recognized that discretization of continuous-time diffusion models for estimation does introduce an estimation bias, but this is relatively small (see [14]). In this case the discrete-time approach (7) allows us to estimate the proposed model where the jump is normally distributed. In this model, the density functions, a mixture of Poisson−Gaussian distribution is generally used to define the jump diffusion model. Here, we employ a Bernoulli approximation, first introduced in [8], to approximates Poisson−Gaussian distribution underlying the discretized (7). We assume that in each time interval either only one jump occurs or no jump occurs. No other information arrivals over this period of time are allowed. This is tenable for short frequency data, e.g., daily stock returns, and may be debatable for data at higher frequencies. As Ball and Torous demonstrate, it provides an approximation procedure which is highly tractable, stable, and convergent [8]. Hence, the discretized (7) can be rewritten aswhere =1, if there is a jump with probability +O and 0 otherwise with probability . All other notations have been defined previously. Since the limit of the Bernoulli process is governed by a Poisson distribution (see the Appendix for some technique details), we can approximate the likelihood function for the Poisson−Gaussian model using a Bernoulli mixture of the normal distributions. Let =(, , , , , , ). Then, the transition probabilities for the stock returns following a Poisson−Gaussian process are written as ):which approximates the true Poisson−Gaussian density with a mixture of normal distributions. Then can be estimated via maximizing the profile pseudo likelihood functionwhich yields estimators of . The maximization problem can be carried out in many scientific computing packages. Note that the likelihood function (10) is complex nonlinear function based on transition probability density; hence the analytical formula for the estimators could not derived and the large sample property of estimators becomes challenging. We will not discuss this aspect any further. For more details, we refer the reader to Xu and Wang [15] or Zheng and Lin [16]. The numerical technique, sequential quadratic programming algorithm (see [17, 18]) is considered to solve this maximization problem. In our implementation, the likelihood function (10) is numerically maximized with the “fmincon” routine embedded in the “Optimization Toolbox” of MATLAB using the sequential quadratic programming algorithm. Furthermore, in [19], maximum likelihood estimation allows the construction of approximate confidence intervals for the parameters of interest and these confidence intervals are asymptotically optimal.

*Remark 1. *The sequential quadratic programming algorithm has been proved to be an excellent nonlinear programming method for solving constrained optimization problems in a variety of statistical models, such as linear models with longitudinal data under inequality restrictions [15], semiparametric regression models with censored data [16], autologistic models and exponential family models for dependent data [20], Cox-Ingersoll-Ross model for the interest rate [21], integer-valued GARCH models [22], and Gaussian stochastic process (GASP) models [23]. Our experience shows that the algorithm performs very well for moderate sample sizes. To gain more confidence in the estimates, we also try “Global Optimization Toolbox” of MATLAB based on the “fmincon” routine. We find that choosing different initial values yields identical results.

Furthermore, we also provide a formal statistical test for the presence of jumps in the stock and stock index returns. Since a pure diffusion model is nested within a combined diffusion and jump model, a likelihood ratio test can be used to test the null hypothesis : stock and stock index returns are normally distributed. The corresponding likelihood ratio test statistic iswhere denotes the maximum likelihood estimates under jump diffusion model and is the maximum likelihood estimates corresponding to the situation when no jump structure occurs (i.e., =0). Under the null hypothesis, the stock returns are consistent with a log-normal diffusion process without jump factor and is asymptotically distributed with 5 degrees of freedom, where denotes the number of parameters to be estimated. More details about asymptotically distribution of the likelihood ratio test statistic can be found in ([24, 25]).

#### 3. Simulation Study

In this section, we conduct a simulation study for second-order jump diffusion model (4) aimed at examining the performance of the estimation methodology. We consider a special case of (4):where , probability of jump =0.5, and initial values =0.01 and =0. The values of the parameters except jump term are given in [1]. We generate 100 replications (paths) of and according to the above design with each replication consisting of (=1260, 2520, 3780, 5040) daily observations and a time step between two consecutive observation equal to =1/252. Then the coefficients and in the drift functions, , , and , in the diffusion function, and and , in the jump factor, can be estimated by the proposed estimation methodology. The empirical sample mean (MEAN), empirical standard deviation (SD), and root mean square error (RMSE) of the coefficient estimators , , , , , , and are reported (Table 1). We can see the mean of the estimators becomes more accurate; meanwhile the empirical standard deviation and root mean square error become smaller as the observations increases from 1260 to 5040.