Research on the relationship between the multifractality and long memory of realized volatility in the SSECI

We examine the multifractal properties of the realized volatility (RV) and realized bipower variation (RBV) series in the Shanghai Stock Exchange Composite Index (SSECI) by using the multifractal detrended fluctuation analysis (MF-DFA) method. We find that there exist distinct multifractal characteristics in the volatility series. The contributions of two different types of source of multifractality, namely, fat-tailed probability distributions and nonlinear temporal correlations, are studied. By using the unit root test, we also find the strength of the multifractality of the volatility time series is insensitive to the sampling frequency but that the long memory of these series is sensitive.     

Market memory and fat tail consequences in option pricing on the expOU stochastic volatility model

The expOU stochastic volatility model is capable of reproducing fairly well most important statistical properties of financial markets daily data. Among them, the presence of multiple time scales in the volatility autocorrelation is perhaps the most relevant which makes appear fat tails in the return distributions. This paper wants to go further on with the expOU model we have studied in Ref. [J. Masoliver, J. Perelló, Quant. Finance 6 (2006) 423] by exploring an aspect of practical interest. Having as a benchmark the parameters estimated from the Dow Jones daily data, we want to compute the price for the European option. This is actually done by Monte Carlo, running a large number of simulations. Our main interest is to “see” the effects of a long-range market memory from our expOU model in its subsequent European call option. We pay attention to the effects of the existence of a broad range of time scales in the volatility. We find that a richer set of time scales brings the price of the option higher. This appears in clear contrast to the presence of memory in the price itself which makes the price of the option cheaper.     

Forecasting volatility of SSEC in Chinese stock market using multifractal analysis

In this paper, taking about 7 years’ high-frequency data of the Shanghai Stock Exchange Composite Index (SSEC) as an example, we propose a daily volatility measure based on the multifractal spectrum of the high-frequency price variability within a trading day. An ARFIMA model is used to depict the dynamics of this multifractal volatility (MFV) measures. The one-day ahead volatility forecasting performances of the MFV model and some other existing volatility models, such as the realized volatility model, stochastic volatility model and GARCH, are evaluated by the superior prediction ability (SPA) test. The empirical results show that under several loss functions, the MFV model obtains the best forecasting accuracy.     

A phenomenological memristor model for short-term/long-term memory

Memristor is considered to be a natural electrical synapse because of its distinct memory property and nanoscale. In recent years, more and more similar behaviors are observed between memristors and biological synapse, e.g., short-term memory (STM) and long-term memory (LTM). The traditional mathematical models are unable to capture the new emerging behaviors. In this article, an updated phenomenological model based on the model of the Hewlett–Packard (HP) Labs has been proposed to capture such new behaviors. The new dynamical memristor model with an improved ion diffusion term can emulate the synapse behavior with forgetting effect, and exhibit the transformation between the STM and the LTM. Further, this model can be used in building new type of neural networks with forgetting ability like biological systems, and it is verified by our experiment with Hopfield neural network.     

On the scaling ranges of detrended fluctuation analysis for long-term memory correlated short series of data

We examine the scaling regime for the detrended fluctuation analysis (DFA)—the most popular method used to detect the presence of long-term memory in data and the fractal structure of time series. First, the scaling range for DFA is studied for uncorrelated data as a function of time series length L$L$ and the correlation coefficient of the linear regression R²$R^2$ at various confidence levels. Next, a similar analysis for artificial short series of data with long-term memory is performed. In both cases the scaling range λ$\lambda$ is found to change linearly—both with L$L$ and R²$R^2$. We show how this dependence can be generalized to a simple unified model describing the relation λ=λ(L,R²,H)$\lambda =\lambda (L,R^2,H)$ where H$H$ (1/2≤H≤1$1/2\le H\le 1$) stands for the Hurst exponent of the long range autocorrelated signal. Our findings should be useful in all applications of DFA technique, particularly for instantaneous (local) DFA where a huge number of short time series has to be analyzed at the same time, without possibility of checking the scaling range in each of them separately.     

The relationship between noise and mode of delivery on recognition memory and working memory

The present research sought to investigate the effect of noise, comparable to that experienced in an aircraft cabin, on cognitive performance in terms of working memory and recognition memory. In addition, and since advancements in technology have long permitted the exchange of information via various media, the present research also sought to investigate the effect of in-cabin aircraft noise on the medium in which the target signal is delivered. Thirty-two participants (19 female), half non-native English speakers with an average age of 21.84 years (SD = 3.16), and all with normal hearing were asked to complete four different memory tasks under two different experimental factors. The first independent variable was noise, with two conditions: no noise versus wideband noise at 80 dBA. The second independent variable was mode of presentation, with two conditions: target signal presented aurally or visually. With the presentation of stimuli presented in a counterbalanced order, the results from a series of mixed repeated measures analyses revealed that working memory appears largely immune from the effects of wideband noise at 80 dBA. In contrast, recognition memory is most vulnerable to the effects of this noise. In terms of mode of delivery, presenting the target signal visually improves recall performance on the recognition memory task and two of the three working memory tasks (not the linguistics working memory task). Also noise had a greater effect on non-native English speakers on the recognition memory task. These results highlight the varying effect of noise on memory, and the benefits of considering alternate methods of presenting information in noisy settings, such as aviation.     

Forecasting volatility in Shanghai and Shenzhen markets based on multifractal analysis

This paper analyzes the multifractality in Shanghai and Shenzhen stock markets using multifractal spectrum analysis and multifractal detrended fluctuation analysis. We find that the main source of multifractality is long-range correlations of large and small fluctuations. Then, we introduce a multifractal volatility measure (MV) and find that by taking MV as daily conditional volatility, the simulated series displayed similar “stylized facts” to the original daily return series. By capturing the dynamics of MV using the ARFIMA model, we find that the out-of-sample forecasting performance of the ARFIMA-MV model is better than some GARCH-class models and the ARFIMA-RV model under some criteria of loss function.     

闪速存储器中应力诱生漏电流的产生机理

通过实验研究了闪速存储器存储单元中应力诱生漏电流(SILC)的产生机理. 研究结果表明，在低电场应力下，其可靠性问题主要是由载流子在氧化层里充放电引起，而在高电场下，陷阱和正电荷辅助的隧穿效应导致浮栅电荷变化是引起闪速存储器失效的主要原因. 分别计算了高场应力和低场应力两种情况下SILC中的稳态电流和瞬态电流的大小. 关键词： 闪速存储器 应力诱生漏电流 电容耦合效应 可靠性     

Scaling and memory in the return intervals of realized volatility

We perform return interval analysis of 1-min realized volatility defined by the sum of absolute high-frequency intraday returns for the Shanghai Stock Exchange Composite Index (SSEC) and 22 constituent stocks of SSEC. The scaling behavior and memory effect of the return intervals between successive realized volatilities above a certain threshold q are carefully investigated. In comparison with the volatility defined by the closest tick prices to the minute marks, the return interval distribution for the realized volatility shows a better scaling behavior since 20 stocks (out of 22 stocks) and the SSEC pass the Kolmogorov-Smirnov (KS) test and exhibit scaling behaviors, among which the scaling function for 8 stocks could be approximated well by a stretched exponential distribution revealed by the KS goodness-of-fit test under the significance level of 5%. The improved scaling behavior is further confirmed by the relation between the fitted exponent γ and the threshold q. In addition, the similarity of the return interval distributions for different stocks is also observed for the realized volatility. The investigation of the conditional probability distribution and the detrended fluctuation analysis (DFA) show that both short-term and long-term memory exists in the return intervals of realized volatility.     

Alternate stresses and temperature variation as factors of influence of ultrasonic vibration on mechanical and functional properties of shape memory alloys

It is known that the main factors in a variation in the shape memory alloy properties under insonation are heating of the material and alternate stresses action. In the present work the experimental study of the mechanical behaviour and functional properties of shape memory alloy under the action of alternate stresses and varying temperature was carried out. The data obtained had demonstrated that an increase in temperature of the sample resulted in a decrease or increase in deformation stress depending on the structural state of the TiNi sample. It was shown that in the case of the alloy in the martensitic state, a decrease in stress was observed, and on the other hand, in the austenitic state an increase in stress took place. It was found that action of alternate stresses led to appearance of strain jumps on the strain–temperature curves during cooling and heating the sample through the temperature range of martensitic transformation under the constant stress. The value of the strain jumps depended on the amplitude of alternate stresses and the completeness of martensitic transformation. It was shown that the heat action of ultrasonic vibration to the mechanical behaviour of shape memory alloys was due to the non-monotonic dependence of yield stress on the temperature. The force action of ultrasonic vibration to the functional properties was caused by formation of additional oriented martensite.     

阻变存储器复合材料界面及电极性质研究

采用基于密度泛函理论的第一性原理对比研究了Cu（111）/HfO₂（001）,Cu（111）/HfO₂（010）,Cu（111）/HfO₂（100）三种复合材料界面模型的失配率、界面束缚能、电荷密度、电子局域函数以及差分电荷密度. 计算结果表明：Cu（111）/HfO₂（010）失配率最小,界面束缚能最大,界面体系相对最稳定;对比电荷密度及电子局域函数图显示,只有HfO₂（010）方向形成的复合材料体系出现了垂直Cu电极方向完整连通的电子通道,表明电子在此方向上具有局域性、连通性,与阻变存储器（RRAM）器件导通方向一致;差分电荷密度图显示,Cu（111）/HfO₂（010）复合材料体系界面处存在电荷密度分布重叠的现象,界面处有电子的相互转移、成键的存在;进一步计算了Cu（111）/HfO₂（010）体系距离界面不同位置的间隙Cu原子形成能,表明越靠近界面Cu原子越容易进入HfO₂ 体内,在外加电压下易发生电化学反应,从而导致Cu导电细丝的形成与断裂. 研究结果可为RRAM存储器的制备及性能的提高提供理论指导和设计工具. 关键词： 阻变存储器 复合材料 界面 电子通道     

Electrical and memory properties of silicon nitride structures with embedded Si nanocrystals

In this work, the electrical and memory behaviour of metal-silicon nitride-silicon structures with an embedded nanocrystalline silicon layer, which either consists of separated silicon nanocrystals, or is a continuous nanocrystalline layer, are presented. The structures were prepared by low-pressure chemical vapour deposition (LPCVD). The effect of the duration of deposition and the structure of the nanocrystalline layer were studied. The writing/erasing behaviour was similar for all the structures, but the retention properties were much worse in the structure with a continuous nanocrystalline layer, than in the structures with separated Si nanocrystals. This indicates that Si nanocrystals play role in charge storage in the studied structures.     

A study about the existence of the leverage effect in stochastic volatility models

The empirical relationship between the return of an asset and the volatility of the asset has been well documented in the financial literature. Named the leverage effect or sometimes risk-premium effect, it is observed in real data that, when the return of the asset decreases, the volatility increases and vice versa.Consequently, it is important to demonstrate that any formulated model for the asset price is capable of generating this effect observed in practice. Furthermore, we need to understand the conditions on the parameters present in the model that guarantee the apparition of the leverage effect.In this paper we analyze two general specifications of stochastic volatility models and their capability of generating the perceived leverage effect. We derive conditions for the apparition of leverage effect in both of these stochastic volatility models. We exemplify using stochastic volatility models used in practice and we explicitly state the conditions for the existence of the leverage effect in these examples.     

Composition, structure and magnetic properties of sputter deposited Ni-Mn-Ga ferromagnetic shape memory thin films

Polycrystalline Ni-Mn-Ga thin films were deposited by the d.c. magnetron sputtering on well-cleaned substrates of Si(1 0 0) and glass at a constant sputtering power of 36 W. We report the influence of sputtering pressure on the composition, structure and magnetic properties of the sputtered thin films. These films display ferromagnetic behaviour only after annealing at an elevated temperature and a maximum saturation magnetization of 335 emu/cc was obtained for the films investigated. Evolution of martensitic microstructure was observed in the annealed thin films with the increase of sputtering pressure. The thermo-magnetic curves exhibited only magnetic transition in the temperature range of 339-374 K. The thin film deposited at high sputtering pressure of 0.025 mbar was found to be ordered L2₁ austenitic phase.     

Pressure effects on magnetic properties and martensitic transformation of Ni–Mn–Sn magnetic shape memory alloys

The mechanism for the effects of pressure on the magnetic properties and the martensitic transformation of Ni-Mn- Sn shape memory alloys is revealed by first-principles calculations. It is found that the total energy difference between paramagnetic and ferromagnetic austenite states plays an important role in the magnetic transition of Ni-Mn-Sn under pressure. The pressure increases the relative stability of the martensite with respect to the anstenite, leading to an increase of the martensitic transformation temperature. Moreover, the effects of pressure on the magnetic properties and the martensitic transformation are discussed based on the electronic structure.     

Scaling and memory effect in volatility return interval of the Chinese stock market

We investigate the probability distribution of the volatility return intervals τ for the Chinese stock market. We rescale both the probability distribution P_q(τ) and the volatility return intervals τ as to obtain a uniform scaling curve for different threshold value q. The scaling curve can be well fitted by the stretched exponential function , which suggests memory exists in τ. To demonstrate the memory effect, we investigate the conditional probability distribution P_q(τ|τ₀), the mean conditional interval 〈τ|τ₀〉 and the cumulative probability distribution of the cluster size of τ. The results show clear clustering effect. We further investigate the persistence probability distribution P_±(t) and find that P₋(t) decays by a power law with the exponent far different from the value 0.5 for the random walk, which further confirms long memory exists in τ. The scaling and long memory effect of τ for the Chinese stock market are similar to those obtained from the United States and the Japanese financial markets.     

Stress analysis, structure and magnetic properties of sputter deposited Ni-Mn-Ga ferromagnetic shape memory thin films

The residual stress instituted in Ni-Mn-Ga thin films during deposition is a key parameter influencing their shape memory applications by affecting its structural and magnetic properties. A series of Ni-Mn-Ga thin films were prepared by dc magnetron sputtering on Si(1 0 0) and glass substrates at four different sputtering powers of 25, 45, 75 and 100 W for systematic investigation of the residual stress and its effect on structure and magnetic properties. The residual stresses in thin films were characterized by a laser scanning technique. The as-deposited films were annealed at 600 °C for 1 h in vacuum for structural and magnetic ordering. The compressive stresses observed in as-deposited films transformed into tensile stresses upon annealing. The annealed films were found to be crystalline and possess mixed phases of both austenite and martensite, exhibiting good soft magnetic properties. It was found that the increase of sputtering power induced coarsening in thin films. Typical saturation magnetization and coercivity values were found to be 330 emu/cm³ and 215 Oe, respectively. The films deposited at 75 and 100 W display both structural and magnetic transitions above room temperature.     

内陷在C_(60)中的氢原子的性质

在短程球形势阱的模型下 ,运用线性变分法并采用B 样条作为展开基函数计算了内陷于C60 几何中心的氢原子能谱和波函数 ,并计算了势阱深度对能谱的影响 ,详细讨论了内陷氢原子表现出的一系列特殊性质 ,从而对低维半导体材料性能的研究提供了有效的数据 ;同时这一工作也表明 ,用线性变分法结合B 样条函数在处理这类问题时是非常有效的。     

Ni/HfO_2/Pt阻变单元特性与机理的研究

研究了Ni/HfO2(10 nm)/Pt存储单元的阻变特性和机理.该器件具有forming-free的性质,还表现出与以往HfO2(3 nm)基器件不同的复杂的非极性阻变特性,并且具有较大的存储窗口值(105).存储单元的低阻态阻值不随单元面积改变,符合导电细丝阻变机理的特征.采用X射线光电子能谱仪分析器件处于低阻态时的阻变层HfO2薄膜的化学组分以及元素的化学态,结果表明,Ni/HfO2/Pt阻变存储器件处于低阻态时的导电细丝是由金属Ni导电细丝和氧空位导电细丝共同形成的.     

Effect of thermo-mechanical process on structure and high temperature shape memory properties of Ti–15Ta–15Zr alloy

The effect of thermo-mechanical treatment on microstructure evolution, martensite transformation, and shape memory behavior of Ti–15Ta–15Zr high temperature shape memory alloy were investigated. Different martensite morphologies were found with different annealing temperatures. The Ti–15Ta–15Zr alloy exhibits almost perfect shape memory recovery strain of 6% after annealing at 973 K for 0.5 h.