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This paper models stochastic process of price time series of $ CSI $ $ 300 $ index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-Nielsen and Shephard model, the lag caused by asynchrony of market information and market microstructure noises are considered, and the problem of lack of long-term dependence is solved. To speed up the valuation process, several machine learning and deep learning algorithms are used to estimate parameter and evaluate forecast results. Tracking historical jumps of different magnitudes offers promising avenues for simulating dynamic price processes and predicting future jumps. Numerical results show that the deterministic component of stochastic volatility processes would always be captured over short and longer-term windows. Research finding could be suitable for influence investors and regulators interested in predicting market dynamics based on high-frequency realized volatility.

Resistive random access memories (ReRAM) have drawn attention of researchers due to their unique properties with applications in in-memory computing, which allows storage and computation in the same unit. This mitigates one of the major limitations in current computing architectures, where for each computation we require to move data from memory to processor or vice versa, which incurs immense amount of energy overheads. Among the various technologies for implementing ReRAM, memristor is considered to be one of the most desirable candidates due to its small size, low power consumption, and high data retention. Such ReRAM systems are often fabricated in the form of crossbar for compact layout. However, they suffer from various challenges, one of the major ones being the sneak-path problem during reading of cell values. The read operation is mostly disturbed by sneak-path currents that can result in incorrect reading of the cell. This paper presents a new approach for reading the cell values in memristive crossbars, which is capable of avoiding erroneous read operations caused by sneak-paths. It also supports parallel operations whereby multiple memristor states can be read in a single cycle. A straightforward approach for reading all the cells in an n×ncrossbar, where the read operation is performed sequentially, requires O(n2)cycles, whereas the proposed approach requires O(n)cycles.

The ability of resistive memory (ReRAM) to naturally conduct vector–matrix multiplication (VMM), which is the primary operation carried out during the training and inference of neural networks, has caught the interest of researchers. The memristor crossbar is one of the desirable architectures to perform VMM because it offers various benefits over other memory technologies, including in-memory computing, low power, and high density. Direct downloading and chip-on-the-loop approaches are typically used to train ReRAM-based neural networks. In these methods, all weight computations are carried out by a host machine, and the computed weights are downloaded in the crossbar. It has been seen that the network does not deliver the same precision as promised by the host system once the weights have been downloaded. This is because crossbars contain a significant number of faulty memristors and suffer from cell resistance variations because of immature manufacturing technologies. As a result, a cell may not be able to take the exact weight values that the host system generates, and may lead to incorrect inferences. Existing techniques for fault-tolerant mapping either involve network retraining or employ a graph-matching strategy that comes with hardware, power, and latency overheads. In this paper, we propose a mapping method to tolerate the effect of defective memristors. In order to lessen the impact of faulty memristors, the mapping is done in a way that allows network weights to cover up faulty memristors. Further, this work prioritizes the different faults based on the frequency of occurrence. The mapping efficiency is found to increase significantly with low power, area and latency overheads in the proposed approach. Experimental analyses show considerable improvement as compared to state-of-the-art works.

Stock trading has tremendous importance not just as a profession but also as an income source for individuals. Many investment account holders use the appreciation of their portfolio (as a combination of stocks or indexes) as income for their retirement years, mostly betting on stocks or indexes with low risk/low volatility. However, every stock-based investment portfolio has an inherent risk to lose money through negative progression and crash. This study presents a novel technique to predict such rare negative events in financial time series (e.g., a drop in the S&P 500 by a certain percent in a designated period of time). We use a time series of approximately seven years (2517 values) of the S&P 500 index stocks with publicly available features: the high, low and close price (HLC). We utilize a Siamese type neural network for pattern recognition in images followed by a bootstrapped image similarity distribution to predict rare events as they pertain to financial market analysis. Extending on literature about rare event classification and stochastic modeling in financial analytics, the proposed method uses a sliding window to store the input features as tabular data (HLC price), creates an image of the time series window, and then uses the feature vector of a pre-trained convolutional neural network (CNN) to leverage pre-event images and predict rare events. This research does not just indicate that our proposed method is capable of distinguishing event images from non-event images, but more importantly, the method is effective even when only limited and strongly imbalanced data is available.

We derived an expression for the floating strike put arithmetic asian options in financial market when the asset is driven by the generalized Barndorff-Nielsen and Shephard model with stochastic volatility. A solution procedure for the resulting partial differential equation is provided using the technique of Mellin transforms.

In this study, the authors propose a cluster‐based trust management model that efficiently detects the malicious nodes and restricts them to be on a route in wireless ad hoc networks. In contrast to previous works, the trust of a node is calculated using various trust attributes having substantial effect on reliable routing in the networks. In the proposed model, each node periodically predicts the value of each trust attribute about other nodes using autoregression. Subsequently, the direct trust is estimated using the weighted combination of trust attributes and it is fine tuned using proportional–integral model. All these recommendation trusts, from common neighbours, are collected and combined by the clusterhead to quantify the trust, and hence the routing is reliable and secure in the proposed model. Simulation results show that the proposed trust model provides better throughput and packet delivery ratio in presence of malicious nodes compared to other existing schemes.

In a realistic market with transaction costs, the option pricing problem is known to lead to solving nonlinear partial differential equations even in the simplest model. The nonlinear term in these partial differential equations (PDE) reflects the presence of transaction costs. In this article we consider an underlying general stochastic volatility model. In this case the market is incomplete and the option price is not unique. Under a particular market completion assumption where we use a traded proxy for the volatility, we obtain a non-linear PDE whose solution provides the option price in the presence of transaction costs. This PDE is studied and under suitable regularity conditions, we prove the existence of strong solutions of the problem.

Option pricing with transaction costs leads to a nonlinear Black-Scholes type equation where the nonlinear term reflects the presence of transaction costs. Under suitable conditions, we prove the existence of weak solutions in a bounded domain and we extend the results to the whole domain using a diagonal process.

We study the general semilinear second-order ODE 𝑢+𝑔(𝑡,𝑢,𝑢)=0 under different two-point boundary conditions. Using the method of upper and lower solutions, we obtain an existence result. Moreover, under a growth condition on 𝑔, we prove that the set of solutions of 𝑢+𝑔(𝑡,𝑢,𝑢)=0 is homeomorphic to the two-dimensional real space.