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

Authors: Tianshun Yan; Yanyong Zhao; Shuanghua Luo
Journal: Discrete Dynamics in Nature and Society
 (2018)

---

## 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.


---

## Body

## 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 by(1)dYt=Xtdt,dXt=μXtdt+σXtdWt,where μ(Xt) and σ(Xt) are the drift and diffusion functions, respectively [1]. Wt is a standard one-dimensional Brownian motion. Yt is directly observable and differentiable. In this model, Yt can also be expressed as the integrated process(2)Yt=Y0+∫0tXudu.For 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 Yt that can be made stationary by differencing. Second, in the context of stock prices, Xu represents stock return and Yt indicates the cumulation of Xu. 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 that(3)dYt=Xtdt,dXt=α0+α1Xtdt+β+τXt-θ2dWtis 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 σ2(Xt)=β+τ(Xt-θ)2 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:(4)dYt=Xtdt,dXt=α0+α1Xtdt+β+τXt-θ2dWt+Jtdπtλ,where the arrival of jumps Jt is governed by the continuous-time Poisson process πt with frequency parameter λ, 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 Jt as well. Consequently, the stock return is the sum of three components. The component (α0+α1Xt)dt represents the instantaneous expected return on the stock. The β+τ(Xt-θ)2dWt part describes the instantaneous variance of the stock return due to the arrival of “normal” information, and the Jtdπt(λ) 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 Yt is generally observable at the time points {ti,i=0,1,⋯,n+1}, while Xt is a nonobservable process. In fact, for the fixed sampling interval Δ=ti-ti-1, the exact distribution of {Yti}i=0n+1 is generally not explicit. An exception is the case where Xt 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 ti=t0+i/52(i=0,1,⋯,n+1) for a given initial time point t0. Based on Yt=Y0+∫0tXsds and the discrete-time observations {Yti}i=0n+1, we have(5)Yti-Yti-1=∫0tiXsds-∫0ti-1Xsds=∫ti-1tiXsds,i=1,2,⋯,n.Then Xti can be approximated by(6)X¯ti=Yti-Yti-1Δ,i=1,2,⋯,n.Naturally, the smaller the time span Δ is, the closer X¯ti is to Xti. In fact, stock prices are usually observed daily or higher frequency.LetΔUti=X¯ti+1-X¯ti and Vti=Wti+1-Wti. According to the independent increment property of the standard Brownian motion, Vti(i=1,2,⋯,n) are independently and normally distributed with mean zero and variance Δ. Therefore, we can write Vti as Vti=Δεti(i=1,2,⋯,n), where {εti}i=1n are independently and identically distributed as the standard normal distribution. Then the Euler discretization of model (4) can be expressed as(7)ΔUti=X¯ti+1-X¯ti≈α0+α1X¯tiΔ+β+τX¯ti-θ2Δεti+JtiμJ,σJΔπtiλ,i=1,2,⋯,n,whereJti is normally distributed with mean μJ and variance σJ2. Δπti(λ) is the discrete-time Poisson increment. We set the mean jump size equal to zero, μJ = 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 as(8)X¯ti+1-X¯ti≈α0+α1X¯tiΔ+β+τX¯ti-θ2Δiεti+JtiIti,i=1,2,⋯,n.where Iti=1, if there is a jump with probability q=λΔ+O(Δ) and 0 otherwise with probability 1-q. 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 Φ=(α0, α1, β, τ, θ, σJ, q). Then, the transition probabilities for the stock returns following a Poisson−Gaussian process are written as f(X¯t+Δ∣X¯t;Φ):(9)fX¯t+Δ∣X¯t;Φ=qexp-X¯t+Δ-X¯t-α0+α1X¯tΔ22β+τX¯t-θ2Δ+σJ2×12πβ+τX¯t-θ2Δ+σJ2+1-qexp-X¯t+Δ-X¯t-α0+α1X¯tΔ22β+τX¯t-θ2Δ×12πβ+τX¯t-θ2Δ,which approximates the true Poisson−Gaussian density with a mixture of normal distributions. Then Φ can be estimated via maximizing the profile pseudo likelihood function(10)ln⁡LΦ=∑i=1nln⁡fX¯ti+1∣X¯ti;Φ,which yields estimators of Φ. 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 hypothesisH0: stock and stock index returns are normally distributed. The corresponding likelihood ratio test statistic is(11)Ω=2ln⁡LX¯;Φ∗-ln⁡LX¯;Φ0,where Φ∗ denotes the maximum likelihood estimates under jump diffusion model and Φ0 is the maximum likelihood estimates corresponding to the situation when no jump structure occurs (i.e., λ=0). Under the null hypothesis, the stock returns are consistent with a log-normal diffusion process without jump factor and Ω is asymptotically distributed χ2 with d-5 degrees of freedom, where d 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):(12)dYt=Xtdt,dXt=0.2-20Xtdt+0.1+10Xt-0.052dWt+Jtdπtλ,where Jt~N(0,0.12), probability of jump q=0.5, and initial values X0=0.01 and Y0=0. The values of the parameters except jump term are given in [1]. We generate 100 replications (paths) of Yt and Xt according to the above design with each replication consisting of n (n=1260, 2520, 3780, 5040) daily observations and a time step between two consecutive observation equal to Δ=1/252. Then the coefficients α0 and α1 in the drift functions, β, τ, and θ, in the diffusion function, and σJ and q, 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 α^0, α^1, β^, τ^, θ^, σ^J, and q^ 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 n increases from 1260 to 5040.Table 1
Parameters estimate, standard error, and root of mean square error for model (12), where α0=0.2, α1=−20, β=0.1, τ=10, θ=0.05, σJ=0.1, and q=0.5.
α ^ 0 α ^ 1 β ^ τ ^ θ ^ σ ^ J q ^ n=1260 MEAN 0.2746 −20.7403 0.0960 10.2741 0.0538 0.1086 0.4663 SD 0.2708 2.6095 0.0264 1.4731 0.0167 0.0069 0.0473 RMSE 0.2856 2.5985 0.0261 1.4763 0.0166 0.0067 0.0471  n=2520 MEAN 0.2372 −20.3026 0.1014 9.8620 0.0496 0.1024 0.4986 SD 0.2250 2.0568 0.0132 1.2086 0.0082 0.0028 0.0222 RMSE 0.2269 2.1638 0.0132 1.2105 0.0082 0.0028 0.0221  n=3780 MEAN 0.2151 −20.1906 0.0994 10.0149 0.0498 0.1006 0.5040 SD 0.2017 1.4665 0.0114 0.8613 0.0069 0.0023 0.0204 RMSE 0.2041 1.4679 0.0114 0.8570 0.0068 0.0023 0.0203  n=5040 MEAN 0.2112 −20.1625 0.0992 9.9446 0.0504 0.1001 0.5026 SD 0.1502 1.4916 0.0093 0.7038 0.0062 0.0023 0.0181 RMSE 0.1498 1.5089 0.0092 0.7098 0.0061 0.0023 0.0184


## 4. Empirical Analysis

### 4.1. Data Source

In this section, we analyze stock market data based on jump diffusion model (4). The data used in this study consists of daily closing stock prices of US companies (Alcoa, American Express, Boeing, Coca Cola, Disney, General Electric, Microsoft, Johnson & Johnson and JP Morgan) between January 25, 2000 and January 25, 2005 and daily stock market indexes between January 1998 and September 2008 from North America, Asia, and Europe: USA (S&P500, NASDAQ, DJIA), UK (FTSE100), Germany (DAX), France (CAC40), Hong Kong (HANG SENG), Australia (SPASX200), Switzerland (SMI), and Taiwan (TWII). The datasets are available at the Yahoo Finance website (https://finance.yahoo.com/). Following the conventional practice in stock market research, we take the logarithmic transformation of the selected data.


### 4.2. Descriptive Statistics

Microsoft Corporation’s daily stock prices from January 25, 2000 to January 25, 2005 are plotted in Figure1. The most striking feature in the figure is the existence of conspicuous spikes in the stock prices and returns. The descriptive statistics are reported (Table 2). The change in stock prices is clear, positively skewed, and has excess kurtosis indicating that leptokurtosis is undeniable, which also predicates the use of a jump diffusion model. The Jarque-Bera test of both dataset rejects the hypothesis of normality. Other stock prices and indexes have similar statistical characterization. We do not enumerate them here.Table 2
Summary statistics.
Statistic P a n e l   A : Y t P a n e l   B : Δ Y t Mean 3.7957 0.0011 Standard deviation 0.4370 0.0314 Skewness −0.1045 7.4655 Excess kurtosis −1.4564 155.9618 Minimum 3.1268 −0.1788 Maximum 4.7173 0.6602 Jarque-Bera 113.2 ∗ ∗ ∗ 1283600 ∗ ∗ ∗ ∗ ∗ ∗ Indicates significance at 1% level. Note: The statistics reported are for log-price (Yt) and change (ΔYt). The Jarque-Bera statistic tests for normality distribution. If the data are normally distributed, the skewness is 0, whereas the kurtosis is equal to 3.Figure 1
Daily closing stock price of Microsoft Corporation from January 25, 2000 to January 25, 2005.
(a)
Daily closing stock prices of Microsoft Corporation (b)
Change in daily closing stock price of Microsoft Corporation


### 4.3. Parameter Estimation and Testing Results

We shall summarize and compare the parameter estimates between second-order diffusion model (3) and second-order jump diffusion model (4) for the stocks and stock indexes (Tables 3 and 4). In addition to the 7 parameters to be estimated (α0 and α1 in instantaneous mean and β, τ, and θ in the volatility component, the variance σJ2 and probability q of jump factor), the standard error, the log-likelihood value (ln L), and the likelihood ratio test statistic Ω are also reported. For each sampled stock and stock index, the first row and the second row present maximum likelihood estimates and corresponding standard errors of model (4), respectively. The third row and the fourth row present maximum likelihood estimates and corresponding standard errors of model (3). Note that the estimates of standard errors of Φ∗ and Φ0 for the maximum likelihood estimation are obtained from the main diagonal of the inverse of the Hessian matrix, respectively.Table 3
Parameter estimates for stocks.
Stock N α 0 α 1 β τ θ σ J q lnL Ω Alcoa 1257 0.1713 −257.9342 0.6380 23.9529 -0.0156 0.0972 0.1135 1718.1 56.18 ∗ ∗ ∗ (0.3494) (7.7857) (0.0588) (3.9444) (0.0167) (0.0164) (0.0540) 0.0504 −258.9853 0.9154 23.2125 −0.0056   1690.0 (0.3294) (8.6151) (0.0465) (8.7870) (0.0165) American 1251 −0.2182 −256.5039 0.1810 75.6013 -0.0038 0.0646 0.4051 1873.8 87.21 ∗ ∗ ∗ Express  (0.1785) (8.6131) (0.0514) (16.0989) (0.0054) (0.0051) (0.1017) −0.3313 −258.0656 0.5884 83.6826 0.00458   1830.2 ( 0.4116) (9.2299) (0.0341) (12.5241) (0.0048) Boeing 1257 −0.2355 −265.9633 0.4276 52.2001 -0.0069 0.0885 0.1011 1899.7 48.24 ∗ ∗ ∗ (0.4224) (7.7640) (0.0655) (5.9056) (0.0067) (0.0257) (0.0760) 0.0139 −263.8557 0.5745 72.6873 −0.0086   1875.6 (0.3656) (8.8101) (0.0303) (12.8031) (0.0056) Coca Cola 1257 0.0434 −249.5004 0.1210 48.5000 -0.0062 0.0580 0.3086 2263.1 135.99 ∗ ∗ ∗ (0.2459) (7.9359) (0.0210) (6.1083) (0.0050) (0.0048) (0.0611) −0.0280 −248.9476 0.3378 79.3847 −0.0103   2195.1 (0.1851) (9.1881) (0.0197) (12.1064) (0.0042) Disney 1257 −0.2449 −260.6646 0.5405 57.6146 0.0107 0.1658 0.0312 1812.0 140.31 ∗ ∗ ∗ (0.9548) (7.7072) (0.0377) (10.0298) (0.0064) (0.0326) (0.0141) −0.2363 −254.5632 0.7399 50.7316 0.0287   1741.8 (1.0434) (7.8464) (0.0664) (20.1801) (0.0091) General 1257 0.0495 −257.2053 0.2935 47.9844 0.0058 0.0779 0.1955 1969.8 110.54 ∗ ∗ ∗ Electric  (0.1662) (8.1046) (0.0431) (7.8468) (0.0070) (0.0092) (0.0664) −0.2494 −260.3366 0.5816 52.8035 0.0048   1914.5 ( 2.0980) (117.4492) (0.1926) (13.7811) (0.0130) Johnson 1257 −0.3607 −257.3816 0.2195 69.4946 0.0032 0.1207 0.0244 2368.4 106.95 ∗ ∗ ∗ &Johnson  (0.0753) (7.2543) (0.0176) (4.0190) (0.0041) (0.0356) (0.0161) −0.4890 −255.5323 0.2457 112.3180 0.0110   2314.9 (0.0743) (8.9071) (0.0162) (23.3878) (0.0029) JP Morgan 1256 0.3209 −257.6896 0.4855 42.1887 0.0162 0.1640 0.0892 1722.0 335.67 ∗ ∗ ∗ (0.2897) (6.8796) (0.0443) (7.8642) (0.0088) (0.0180) (0.0252) 0.1524 −260.0101 1.0547 42.8909 −0.0307   1554.2 (1.1554) (21.8999) (0.0803) (92.4838) (0.0503) Microsoft 1257 0.4073 −267.0635 0.5217 52.5552 0.0041 0.3059 0.0370 1761.8 570.82 ∗ ∗ ∗ (0.3472) (8.1481) (0.0452) (11.3793) (0.0069) (0.0491) (0.0119) 0.6737 −267.9022 0.6690 264.2013 −0.0316   1476.4 (0.1681) (11.8308) (0.0409) (31.7660) (0.0036) ∗ ∗ ∗ Indicates significance at 1% level. a Standard errors of maximum likelihood estimates are in parentheses.Table 4
Parameter estimates for stock indexes.
Stock index N α 0 α 1 β τ θ σ J q lnL Ω CAC40 2798 0.2383 −257.6798 0.1330 54.8009 0.0116 0.0627 0.1645 5376.7 344.59 ∗ ∗ ∗ ( 0.1155) (4.9044) (0.0103) (5.0972) (0.0029) (0.0043) (0.0285) −0.0615 −257.9421 0.2687 82.9046 0.0093   5204.4 ( 0.0978) (6.3565) (0.0104) (7.5923) (0.0024) DAX 2785 0.2784 −255.8185 0.1555 57.6903 0.0127 0.0645 0.1652 5164.5 280.85 ∗ ∗ ∗ ( 0.2202) (24.7216) (0.0167) (17.3980) (0.0031) (0.0039) (0.0300) −0.0118 −252.6514 0.2919 86.8931 0.0124   5024.1 ( 0.1394) (6.1700) (0.0110) (9.5216) (0.0023) DJIA 2703 0.1067 −210.7317 0.0590 47.0208 0.0082 0.0430 0.1481 6373.6 317.71 ∗ ∗ ∗ ( 0.0605) (5.4284) (0.0048) (8.5147) (0.0021) (0.0035) (0.0263) 0.0699 −211.6276 0.1050 66.2194 0.0168   6214.8 ( 0.1136) (5.6852) (0.0041) (8.1033) (0.0022) FTSE100 2713 −0.1870 −260.5673 0.0649 61.1198 −0.0018 0.0379 0.3053 5841.7 222.60 ∗ ∗ ∗ (0.3935) (9.5712) (0.0387) (31.0472) (0.0030) (0.0084) (0.2009) −0.1749 −264.5221 0.1641 79.7363 −0.0028   5730.4 ( 0.4693) (18.1376) (0.0456) (65.8072) (0.0018) HANGSENG 2702 0.1166 −245.2607 0.1369 41.1688 0.0186 0.0655 0.2412 4863.4 366.22 ∗ ∗ ∗ ( 1.5002) (7.6386) (0.1075) (8.8111) (0.0167) (0.0125) (0.1701) −0.0341 −238.4197 0.3550 81.9699 0.0035   4683.4 (0.1558) (6.8947) (0.0132) (12.5799) (0.0028) NASDAQ 2767 0.4242 −253.6221 0.1810 66.7837 0.0047 0.0658 0.2204 4740.6 238.63 ∗ ∗ ∗ (0.1197) (6.3600) (0.0143) (9.0416) (0.0033) (0.0043) (0.0312) −0.3926 −255.9448 0.3824 91.8324 0.0070   4621.3 ( 0.0814) (7.9904) (0.0177) (19.2416) (0.0028) SP500 2804 0.1679 −261.8900 0.0441 24.8916 0.0235 0.0360 0.4098 6122.3 278.23 ∗ ∗ ∗ (0.1935) (5.3790) (0.0108) (4.8198) (0.0050) (0.0022) (0.0706) −0.0194 −261.4696 0.1745 43.5322 0.0093   5983.2 (0.1217) (5.5881) (0.0072) (4.1993) (0.0031) SPASX200 2870 0.2102 −257.0073 0.0570 71.0473 0.0061 0.0514 0.0785 6951.4 389.95 ∗ ∗ ∗ (0.1332) (6.5243) (0.0035) (6.9430) (0.0014) (0.0052) (0.0180) 0.0966 −253.5373 0.0908 112.8947 0.0093   6756.5 (0.0567) (6.6217) (0.0034) (8.9553) (0.0011) SMI 2704 0.1878 −255.8562 0.0000 0.1737 0.8342 0.0526 0.1877 5684.6 39.95 ∗ ∗ ∗ (0.1344) (4.5107) (0.0386) (0.0315) (0.1503) (0.0037) (0.0266) 0.0394 −247.5534 0.1492 113.4965 0.0079   5664.6 (0.1479) (6.3341) (0.0062) (12.8102) (0.0014) TWII 2639 −0.0621 −240.4190 0.1094 21.5185 0.0008 0.0508 0.4276 4836.2 238.07 ∗ ∗ ∗ (0.1073) (5.0817) (0.0153) (3.9170) (0.0065) (0.0023) (0.0457) −0.0273 −245.3496 0.3676 31.7291 0.0037   4717.1 (0.4595) (5.6751) (0.0111) (2.9732) (0.0056) ∗ ∗ ∗ Indicates significance at 1% level. a Standard errors of maximum likelihood estimates are in parentheses.It can be seen that the standard errors of parameter estimates for jump diffusion model (4) are extremely small, confirming the preferability of the estimation methodology, and are mostly smaller than that of pure diffusion model (3) (Tables 3 and 4). For all the stocks and stock indexes, the corresponding log-likelihood values, ln L, of model (4) are also higher than that of model (3). Furthermore, the likelihood ratio test demonstrates the presence of jumps at the 1 percent significance level for each stock and stock index. These empirical results show that the second-order jump diffusion model (4) is more suitable for modeling the stock market returns from North America, Asia, and Europe than second-order pure diffusion model (3).


## 4.1. Data Source

In this section, we analyze stock market data based on jump diffusion model (4). The data used in this study consists of daily closing stock prices of US companies (Alcoa, American Express, Boeing, Coca Cola, Disney, General Electric, Microsoft, Johnson & Johnson and JP Morgan) between January 25, 2000 and January 25, 2005 and daily stock market indexes between January 1998 and September 2008 from North America, Asia, and Europe: USA (S&P500, NASDAQ, DJIA), UK (FTSE100), Germany (DAX), France (CAC40), Hong Kong (HANG SENG), Australia (SPASX200), Switzerland (SMI), and Taiwan (TWII). The datasets are available at the Yahoo Finance website (https://finance.yahoo.com/). Following the conventional practice in stock market research, we take the logarithmic transformation of the selected data.


## 4.2. Descriptive Statistics

Microsoft Corporation’s daily stock prices from January 25, 2000 to January 25, 2005 are plotted in Figure1. The most striking feature in the figure is the existence of conspicuous spikes in the stock prices and returns. The descriptive statistics are reported (Table 2). The change in stock prices is clear, positively skewed, and has excess kurtosis indicating that leptokurtosis is undeniable, which also predicates the use of a jump diffusion model. The Jarque-Bera test of both dataset rejects the hypothesis of normality. Other stock prices and indexes have similar statistical characterization. We do not enumerate them here.Table 2
Summary statistics.
Statistic P a n e l   A : Y t P a n e l   B : Δ Y t Mean 3.7957 0.0011 Standard deviation 0.4370 0.0314 Skewness −0.1045 7.4655 Excess kurtosis −1.4564 155.9618 Minimum 3.1268 −0.1788 Maximum 4.7173 0.6602 Jarque-Bera 113.2 ∗ ∗ ∗ 1283600 ∗ ∗ ∗ ∗ ∗ ∗ Indicates significance at 1% level. Note: The statistics reported are for log-price (Yt) and change (ΔYt). The Jarque-Bera statistic tests for normality distribution. If the data are normally distributed, the skewness is 0, whereas the kurtosis is equal to 3.Figure 1
Daily closing stock price of Microsoft Corporation from January 25, 2000 to January 25, 2005.
(a)
Daily closing stock prices of Microsoft Corporation (b)
Change in daily closing stock price of Microsoft Corporation


## 4.3. Parameter Estimation and Testing Results

We shall summarize and compare the parameter estimates between second-order diffusion model (3) and second-order jump diffusion model (4) for the stocks and stock indexes (Tables 3 and 4). In addition to the 7 parameters to be estimated (α0 and α1 in instantaneous mean and β, τ, and θ in the volatility component, the variance σJ2 and probability q of jump factor), the standard error, the log-likelihood value (ln L), and the likelihood ratio test statistic Ω are also reported. For each sampled stock and stock index, the first row and the second row present maximum likelihood estimates and corresponding standard errors of model (4), respectively. The third row and the fourth row present maximum likelihood estimates and corresponding standard errors of model (3). Note that the estimates of standard errors of Φ∗ and Φ0 for the maximum likelihood estimation are obtained from the main diagonal of the inverse of the Hessian matrix, respectively.Table 3
Parameter estimates for stocks.
Stock N α 0 α 1 β τ θ σ J q lnL Ω Alcoa 1257 0.1713 −257.9342 0.6380 23.9529 -0.0156 0.0972 0.1135 1718.1 56.18 ∗ ∗ ∗ (0.3494) (7.7857) (0.0588) (3.9444) (0.0167) (0.0164) (0.0540) 0.0504 −258.9853 0.9154 23.2125 −0.0056   1690.0 (0.3294) (8.6151) (0.0465) (8.7870) (0.0165) American 1251 −0.2182 −256.5039 0.1810 75.6013 -0.0038 0.0646 0.4051 1873.8 87.21 ∗ ∗ ∗ Express  (0.1785) (8.6131) (0.0514) (16.0989) (0.0054) (0.0051) (0.1017) −0.3313 −258.0656 0.5884 83.6826 0.00458   1830.2 ( 0.4116) (9.2299) (0.0341) (12.5241) (0.0048) Boeing 1257 −0.2355 −265.9633 0.4276 52.2001 -0.0069 0.0885 0.1011 1899.7 48.24 ∗ ∗ ∗ (0.4224) (7.7640) (0.0655) (5.9056) (0.0067) (0.0257) (0.0760) 0.0139 −263.8557 0.5745 72.6873 −0.0086   1875.6 (0.3656) (8.8101) (0.0303) (12.8031) (0.0056) Coca Cola 1257 0.0434 −249.5004 0.1210 48.5000 -0.0062 0.0580 0.3086 2263.1 135.99 ∗ ∗ ∗ (0.2459) (7.9359) (0.0210) (6.1083) (0.0050) (0.0048) (0.0611) −0.0280 −248.9476 0.3378 79.3847 −0.0103   2195.1 (0.1851) (9.1881) (0.0197) (12.1064) (0.0042) Disney 1257 −0.2449 −260.6646 0.5405 57.6146 0.0107 0.1658 0.0312 1812.0 140.31 ∗ ∗ ∗ (0.9548) (7.7072) (0.0377) (10.0298) (0.0064) (0.0326) (0.0141) −0.2363 −254.5632 0.7399 50.7316 0.0287   1741.8 (1.0434) (7.8464) (0.0664) (20.1801) (0.0091) General 1257 0.0495 −257.2053 0.2935 47.9844 0.0058 0.0779 0.1955 1969.8 110.54 ∗ ∗ ∗ Electric  (0.1662) (8.1046) (0.0431) (7.8468) (0.0070) (0.0092) (0.0664) −0.2494 −260.3366 0.5816 52.8035 0.0048   1914.5 ( 2.0980) (117.4492) (0.1926) (13.7811) (0.0130) Johnson 1257 −0.3607 −257.3816 0.2195 69.4946 0.0032 0.1207 0.0244 2368.4 106.95 ∗ ∗ ∗ &Johnson  (0.0753) (7.2543) (0.0176) (4.0190) (0.0041) (0.0356) (0.0161) −0.4890 −255.5323 0.2457 112.3180 0.0110   2314.9 (0.0743) (8.9071) (0.0162) (23.3878) (0.0029) JP Morgan 1256 0.3209 −257.6896 0.4855 42.1887 0.0162 0.1640 0.0892 1722.0 335.67 ∗ ∗ ∗ (0.2897) (6.8796) (0.0443) (7.8642) (0.0088) (0.0180) (0.0252) 0.1524 −260.0101 1.0547 42.8909 −0.0307   1554.2 (1.1554) (21.8999) (0.0803) (92.4838) (0.0503) Microsoft 1257 0.4073 −267.0635 0.5217 52.5552 0.0041 0.3059 0.0370 1761.8 570.82 ∗ ∗ ∗ (0.3472) (8.1481) (0.0452) (11.3793) (0.0069) (0.0491) (0.0119) 0.6737 −267.9022 0.6690 264.2013 −0.0316   1476.4 (0.1681) (11.8308) (0.0409) (31.7660) (0.0036) ∗ ∗ ∗ Indicates significance at 1% level. a Standard errors of maximum likelihood estimates are in parentheses.Table 4
Parameter estimates for stock indexes.
Stock index N α 0 α 1 β τ θ σ J q lnL Ω CAC40 2798 0.2383 −257.6798 0.1330 54.8009 0.0116 0.0627 0.1645 5376.7 344.59 ∗ ∗ ∗ ( 0.1155) (4.9044) (0.0103) (5.0972) (0.0029) (0.0043) (0.0285) −0.0615 −257.9421 0.2687 82.9046 0.0093   5204.4 ( 0.0978) (6.3565) (0.0104) (7.5923) (0.0024) DAX 2785 0.2784 −255.8185 0.1555 57.6903 0.0127 0.0645 0.1652 5164.5 280.85 ∗ ∗ ∗ ( 0.2202) (24.7216) (0.0167) (17.3980) (0.0031) (0.0039) (0.0300) −0.0118 −252.6514 0.2919 86.8931 0.0124   5024.1 ( 0.1394) (6.1700) (0.0110) (9.5216) (0.0023) DJIA 2703 0.1067 −210.7317 0.0590 47.0208 0.0082 0.0430 0.1481 6373.6 317.71 ∗ ∗ ∗ ( 0.0605) (5.4284) (0.0048) (8.5147) (0.0021) (0.0035) (0.0263) 0.0699 −211.6276 0.1050 66.2194 0.0168   6214.8 ( 0.1136) (5.6852) (0.0041) (8.1033) (0.0022) FTSE100 2713 −0.1870 −260.5673 0.0649 61.1198 −0.0018 0.0379 0.3053 5841.7 222.60 ∗ ∗ ∗ (0.3935) (9.5712) (0.0387) (31.0472) (0.0030) (0.0084) (0.2009) −0.1749 −264.5221 0.1641 79.7363 −0.0028   5730.4 ( 0.4693) (18.1376) (0.0456) (65.8072) (0.0018) HANGSENG 2702 0.1166 −245.2607 0.1369 41.1688 0.0186 0.0655 0.2412 4863.4 366.22 ∗ ∗ ∗ ( 1.5002) (7.6386) (0.1075) (8.8111) (0.0167) (0.0125) (0.1701) −0.0341 −238.4197 0.3550 81.9699 0.0035   4683.4 (0.1558) (6.8947) (0.0132) (12.5799) (0.0028) NASDAQ 2767 0.4242 −253.6221 0.1810 66.7837 0.0047 0.0658 0.2204 4740.6 238.63 ∗ ∗ ∗ (0.1197) (6.3600) (0.0143) (9.0416) (0.0033) (0.0043) (0.0312) −0.3926 −255.9448 0.3824 91.8324 0.0070   4621.3 ( 0.0814) (7.9904) (0.0177) (19.2416) (0.0028) SP500 2804 0.1679 −261.8900 0.0441 24.8916 0.0235 0.0360 0.4098 6122.3 278.23 ∗ ∗ ∗ (0.1935) (5.3790) (0.0108) (4.8198) (0.0050) (0.0022) (0.0706) −0.0194 −261.4696 0.1745 43.5322 0.0093   5983.2 (0.1217) (5.5881) (0.0072) (4.1993) (0.0031) SPASX200 2870 0.2102 −257.0073 0.0570 71.0473 0.0061 0.0514 0.0785 6951.4 389.95 ∗ ∗ ∗ (0.1332) (6.5243) (0.0035) (6.9430) (0.0014) (0.0052) (0.0180) 0.0966 −253.5373 0.0908 112.8947 0.0093   6756.5 (0.0567) (6.6217) (0.0034) (8.9553) (0.0011) SMI 2704 0.1878 −255.8562 0.0000 0.1737 0.8342 0.0526 0.1877 5684.6 39.95 ∗ ∗ ∗ (0.1344) (4.5107) (0.0386) (0.0315) (0.1503) (0.0037) (0.0266) 0.0394 −247.5534 0.1492 113.4965 0.0079   5664.6 (0.1479) (6.3341) (0.0062) (12.8102) (0.0014) TWII 2639 −0.0621 −240.4190 0.1094 21.5185 0.0008 0.0508 0.4276 4836.2 238.07 ∗ ∗ ∗ (0.1073) (5.0817) (0.0153) (3.9170) (0.0065) (0.0023) (0.0457) −0.0273 −245.3496 0.3676 31.7291 0.0037   4717.1 (0.4595) (5.6751) (0.0111) (2.9732) (0.0056) ∗ ∗ ∗ Indicates significance at 1% level. a Standard errors of maximum likelihood estimates are in parentheses.It can be seen that the standard errors of parameter estimates for jump diffusion model (4) are extremely small, confirming the preferability of the estimation methodology, and are mostly smaller than that of pure diffusion model (3) (Tables 3 and 4). For all the stocks and stock indexes, the corresponding log-likelihood values, ln L, of model (4) are also higher than that of model (3). Furthermore, the likelihood ratio test demonstrates the presence of jumps at the 1 percent significance level for each stock and stock index. These empirical results show that the second-order jump diffusion model (4) is more suitable for modeling the stock market returns from North America, Asia, and Europe than second-order pure diffusion model (3).


## 5. Conclusion and Discussion

In this paper, a new second-order jump diffusion model is proposed to investigate the dynamic characteristic of stock market returns from North America, Asia, and Europe. The maximum likelihood approach is employed to estimate parameters of the model. The simulation study demonstrates that the estimation approach works satisfactorily. By analyzing stock market data from North America, Asia, and Europe, we find that the second-order jump diffusion model can capture high levels of kurtosis and skewness of stock return distributions, while the second-order diffusion model that ignores the jump factor is misspecified. The likelihood ratio test also confirms the statistically significant presence of jump factor in the selected stock market returns.It should be pointed out that there are some topics that can be further considered. This paper mainly focuses on the Poisson jump. In fact, there are some random perturbations, such as the telephone noise which can be further studied [26]. Besides, stochastic differential equations have been recently applied into the SIS and SIRS epidemic models with Markov-switching [27–29], the budworm growth model with regime switching [30], and stochastic regime switching predator-prey model [31, 32]. It is very promising to incorporate the jump factors into these stochastic models.



---

*Source: 9549707-2018-06-13.xml*