processes for CSI300 index. These two time series can be fitted well with NGARCH system. There are many advantages for choosing basis series to re- place the futures series in analyzing the Samuelson effects. Above all, we can in- fer the optimal hedge ratio and hedge effectiveness easily. We conclude that there is no statistical evidence to support the Samuelson effect for the process of the CSI300 index futures. Besides, the variable of time to maturity for the CSI300 index futures constitutes an influence on the hedge effectiveness and the op- timal hedge ratio. In general, ignoring the time-varying volatility and the effect of maturity will make the result of over-hedging. Finally, the hedge effectiveness is significantly better with the consideration of maturity effect than without the maturity effect. This paper contributes to the financial practices in the following ways. First, as the margin requirement is positively related to the futures price volatility, the relationship between time to maturity and volatility process has essential implication for margin pricing in CSI300 index futures markets. Second, hedgers are able to perform hedging strategies to minimize portfolio’s volatility by replacing among various futures contracts with different time to maturity. Finally, the time varying volatility is a pivot input for option pricing, the interaction between volatility and time to maturity also has implications for pricing options on futures.
The mean value of VaR represents the average for all of the one-day-ahead out-of-sample VaR forecasting based on the historical simulation method, GARCH model and CARR model and displays their results in column 3, 4 and 7 respectively. The number of out-of-sample size is 597. In Table 2, regardless of the CSI-300 spot or futures index, the mean value of VaRs that derived by his- torical simulation approach are greater in absolute value than the mean value of VaRs for the method of GARCH and CARR models. It represents that the his- torical simulation approach for VaR measurement is more conservative than GARCH and CARR methods. Besides, Ross  pointed out the futures trading transmits more information than its spot market. Thus, it is reasonable to obtain the oscillation is more volatile for the CSI-300 futures market than the CSI-300 spot market. The one-day-ahead VaR expected values are bigger in absolute val- ue for CSI-300 futures than its corresponding CSI-300 spot index.
Using high-frequency trading data at the minute level during April 16, 2010 to February 28, 2011, we were able to divide the trading of futures during extended hours into three parts: post-closing trading session (15:00-15:15pm), overnight session (15:15-9:15, next day), and pre-opening trading session (9:15-9:30). Our analyses generate empirical evidence as follows. First, we confirm that the underlying spot equity market is the main information source of the futures market. Specifically, trading of futures contracts during a post-closing session is less active and conveys less information than it does when the spot market opens. In addition, the three trading sessions of CSI300 index futures are significantly correlated with the overnight spot CSI300 index return. Second, we find significant explanatory power of the futures return innovations of the post-closing and pre-opening sessions on overnight spot returns. This suggests that post-closing and pre-opening sessions convey private information about
The time series of share prices is a highly noised, non-stationary chaotic system which possesses both linear and non-linear characteristics. The alternative of either linear or non-linear prediction models is of its inherent limitation. The paper establishes an ARIMA and RBF-ANN combined mod- el and makes a short-term prediction on the time series of CSI300 index by choosing various typi- cal input variables. Results show that the combined model with multiple input indicators, com- pared with single ARIMA model, single RBF-ANN model, or models with single input variable, is of higher precision.
As an effective way in finding the underlying parameters of a high-dimension space, manifold learning is popular in nonlinear dimensionality reduction which makes high-dimensional data easily to be observed and analyzed. In this pa- per, Isomap, one of the most famous manifold learning algorithms, is applied to process closing prices of stocks of CSI300 index from September 2009 to October 2011. Results indicate that Isomap algorithm not only reduces dimensiona- lity of stock data successfully, but also classifies most stocks according to their trends efficiently.
inus based on FTSE 100 index tested a series of econometric model, including unit root test, vector error correction model, the establishment of such GARCH model and found that the introduction of stock index fu- tures, increased the volatility of the spot prices stock, that is on the spot markets had a negative impact. Domestic scholars Tian-cai Xing and Ge Zhang based on FTSE Xinhua A50 index futures, through the establishment of GARCH model, analyzes its launch on the CSI300 stock index, the results found that the introduction of stock index futures slightly increases the volatility of the spot market, but this impact is very small. In 2010, they se- lected the CSI300 index futures trading simulation data, through the establishment of GARCH model to test re- sidual ARCH effects, etc., confirmed the launch of stock index futures on the volatility of the spot market is not greater impact. But because of the data from the simula- tion trading system, therefore the reliability of the con- clusions still needs to be fastidious.
Our data is obtained from various financial sources online. Historical daily turnover and prices of the Hang Seng Index (HSI) from 1 December 2008 to 31 December 2012 are ob- tained from Quamnet. Data for the short-selling volume is obtained from Yahoo Finance. The daily Hong Kong Interbank Offered Rate (HIBOR) is obtained from the Hong Kong Monetary Authority. Historical data for the S&P 500, the Nikkei 225, and the CSI300 in- dices are gathered from Yahoo Finance. The stock market sentiment index is estimated using the principal-component method.
The year 2008 witnessed the greatest joint stock reform and financial crisis in Chinese history. After these two cases, significant changes have taken place in investors’ behaviors worldwide, along with which is the occurrence of structure change in stock market. In this paper, we employ Copula model to simulate the joint distribution between Shanghai Stock Index (SSE) and Chinese Shanghai Index 300 (CSI300), to find out structure change in Chinese stock market before and after 2008. From results of empirical studies, we get conclusions that the main nature of Chinese stocks market is symmetric, in both marginal and joint distributions. Via the changes of Copula types, upper and lower tail coefficients and Kendall coefficients, we can measure the structure change in Chinese stock market, and get further con- clusion about investors’ behaviors change. Before 2008, there is an equal power in quitting market and longing, while diversified investors adjusted their expectation uniformly after this year. Testing results show that the general depend- ence structure of CSI300 and SSE is highly dependent and symmetric in most cases. From the distribution of upper and lower tail coefficients, we can draw the conclusion that stratified investors are mainly focused on two tasks, after this year, to close the position on stocks with high correlated stocks market and to maintain market value of stocks.
In addition to the CSI300 index, we have also examined the SSE and SZSE composite indices, which contain 1381 and 2057 stocks, respectively, during our sample period, and tested whether cross-sectional dispersion helps generate more precise volatility forecasts in these markets. Table 12 summarizes the results for the SSE composite index. In this table, we again observe qualitatively similar results in that the dispersion measures continue to offer information content that helps reduce volatility prediction error, in many cases significantly, and improves the adjusted R 2 for the Mincer-Zarnowitz regressions. Furthermore, we have conducted the same exercise for the SZSE composite index (Table A1), and the subsample analysis for the SSE composite index (Tables A2 and A3) and the SZSE composite index (Tables A4 and A5) in the appended tables. All results attest to the importance of incorporating cross-sectional dispersion in volatility forecasting.
The construction of the CSI-R was done through itera- tive discussions between authors and several groups of experienced clinicians until consensuses were reached and face validity was deemed acceptable. The resulting new CSI-R in overview is found on the right in Table 1. Finally, the scale introductory text and item text was phrased for use in two ways, one for the use of external reviewers and one for clinicians’ own assessment of RACT fidelity. The CSI-R was field tested for validity in a series of yearly peer reviews at services practicing RACT based case management, and the wording was successively improved in an iterative process, until face validity was deemed satisfactory.
As it is not convenient for a subject to carry a device, Jie et al.  implement a localization system with non-equipped entity. Some other systems [26–30] have been proposed which constitute fingerprint-based or model-based solutions. Model-based algorithms, which do not require any laborious effort to build and main- tain a radio map, estimate the distance from the object to an AP by using statistical models. LiFS  achieves the accuracy of about 1 m in indoor environments by using selected subcarriers of CSI to build a model. Due to the changes of direction, reflecting, or scattering sig- nals, it is hard to obtain an accurate relationship be- tween the signal and the location. Fingerprint-based algorithms, however, need no prior knowledge of the relationship between the distance and signals, gain much attention in the localization systems. General fingerprint-based technique has two phases named the offline and localization phase. Offline phase is to collect signals for generating fingerprints from every spot of an interested area to build a radio map. During the localization phase, observed fingerprint is matched against the radio-map by using matching algorithms. Zhou et al.  establish the nonlinear relationship be- tween CSI fingerprints during the offline phase and es- timate locations through SVM regression. Unlike fingerprinting localization, MaTrack  uses Dynamic-MUSIC method to identify the angles for localization through detecting the subtle reflection sig- nals from the human body.
Abstract—Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. When the eavesdropping channel state information (CSI) is available at BS, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non- convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. When the CSI of the eavesdropping channel is not available, we first consider the case that the secure user is not the farthest from BS, and the transmit power of the farther users is maximized via joint precoding optimization to guarantee its security. Then, we consider the case when the farthest user from BS requires secure transmission, and the modified successive interference cancellation order and joint precoding optimization can be adopted to ensure its security. Similar method can be exploited to solve the two non-convex problems when the CSI is unknown. Simulation results demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively, with and without eavesdropping CSI.
The PUCCH format 3 supports transmission of 48 coded bits. The actual number of bits of HARQ-ACK is determined from the number of configured CCs, the configured transmission modes on each of them, and, in TDD, the HARQ-ACK bundling window size (the number of downlink subframes associated with a single uplink subframe). For TDD, PUCCH format 3 supports a HARQ-ACK payload size of up to 20 bits. If the number of HARQ-ACK bits to be fed back for multiple downlink subframes from multiple CCs is greater than 20, ‘spatial bundling’ of the HARQ-ACK bits corresponding to the two codewords within a downlink subframe is performed for each of the configured CC to reduce the HARQ-ACK bits. Thus, the HARQ-ACK bits may be less than 20 bits and can be fed back on PUCCH format 3. However, a UE not only need to feedback HARQ-ACK bits from multiple downlink CCs, but also should report CSI about each configured CC. So, when HARQ-ACK bits collide with CSI [11, 12], a new challenge appears.
Data collection: At the time of measurements, a TP-LINK TLWR741N wireless router is used as transmitter operating in IEEE802.11n AP mode at 2.4GHz. We use two receiver setups: a LENOVO laptop equipped with Intel 5300 NIC and a mini PC with external Intel 5300 NIC to take device diversity into consideration. The firmware is modified and the receiver pings packets from the AP to collect CSI measurements. A group of 30 CSIs are extracted from each packet and processed. To simulate natural human mobility, the receiver is placed on a wheeled desk of 0.8 m in height, and is pushed by two different volunteers. For each measurement, the receiver moves randomly within the range of 1m at a speed from 0.5 m/s to 2m/s. A Smartphone is attached to the receiver to record acceleration traces to measure the average speeds of movements. The ground truth is manually determined for each and every test location based on whether a direct straight line exists between transmitter and the receiver.
In 2003, women made up 47% of the total U.S. workforce (McBride-Stetson, 2004, p. 239). However, they held only 19% of the science, engineering and technology posts in the U.S. (Thom, 2001, p. 171). CSI: Las Vegas of this 19%. Sara is a single, 30-something loner who sits around listening to a police scanner. Catherine is an ex-stripper and a sometimes single, 40-something mother. Despite their differences Sara and Catherine are both successful forensic scientists. They have resisted patriarchal pressures to opt for careers with higher concentrations of women that are tightly tied to women’s traditional roles as wives and mothers, such as nursing and teaching. Sara and Catherine also exercise a high degree of agency but like the show’s other female characters who exhibit independence and autonomy they do not fare as well as their male counterparts. Significantly, in CSI: Las Vegas self-determined women frequently are portrayed as fractured if not atomized under the male gaze. Sara and Catherine are among the show’s broken women. Their commitment to forensic science and their jobs is represented in ambivalent terms: Sara and Catherine are depicted as empowered and authoritative but also lacking, which is consistent with patriarchal assessments of women who are successful in nontraditional endeavors. Moreover, Sara and Catherine, because they resist patriarchal norms by performing a “man’s” job, are subject to a persistently invasive voyeurism—the women’s private lives become the focus of public scrutiny—that discourages similar career choices among female audience members.