Volatility Factors Research Articles

A Study on Stock Analysis of Automobile Industries

This study investigates the stock movement of automobile companies listed on the NIFTY50 index, focusing on five major firms: TVS Motor, MRF, Ashok Leyland, Maruti Suzuki, and Tata Motors. Using quarterly and annual data from January 2021 to December 2021, the analysis identifies key trends and volatility factors impacting stock prices in the automotive sector. Key findings suggest that fluctuations in stock prices are influenced by quarterly performance, industry trends, and macroeconomic conditions. Additionally, the report highlights the impact of the COVID-19 pandemic on the sector, with companies facing challenges such as reduced demand, increased costs, and supply chain disruptions. The study concludes that stock movements in this industry are volatile, urging investors to monitor these fluctuations closely before making investment decisions.

Improved ant colony algorithm for path planning based on pheromone difference distribution strategy

In view of the problems of blind search in the initial stage, slow convergence speed and easy to fall into local optimum when the traditional ant colony algorithm is used for mobile robot path planning, an improved ant colony algorithm is proposed. Firstly, according to the distance between each node and the line connecting the starting point and the target point, the initial pheromone is unevenly distributed to make it normally distributed, which reduces the blindness of the algorithm search in the initial stage and speeds up the search for the optimal solution; secondly, the volatility factor is improved, and the principle of double volatility factor is adopted to control the volatility of pheromone, which not only reduces the possibility of local optimum but also speeds up the convergence speed; the redundant path is further optimized to make the path better. Simulation results show that the improved ant colony algorithm in this paper converges faster and more stably than the traditional ant colony algorithm and other improved ant colony algorithms.

An Empirical Study on Asset Pricing Models Based on The Chinese A-Share Market

Based on the analysis of the A-share markets in Shanghai and Shenzhen Stock Exchange from 2000 to 2023, this paper systematically evaluates the applicability and explanatory power of the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model (FF3) and the Fama-French Five-Factor Model (FF5) in the Chinese market. The study finds that although CAPM has some explanatory power in the early stage, the three-factor and five-factor models can better explain the cross-sectional changes in stock returns as the market develops. In the descriptive statistics, each factor's volatility and correlation reveal the Chinese market's unique characteristics, such as the relative strength of small-cap and growth stocks. In the regression analysis, the results of the Fama-MacBeth regression showed that the significance and explanatory power of the market factor, size factor and value factor were different in different models. Further robustness analysis reveals the COVID-19's impact on the model's performance through the sub-period analysis. This paper provides a new perspective on understanding the asset pricing of China's A-share market and a theoretical basis for investors' investment decisions. It also points out the limitations of existing models in explaining emerging market equity returns. It suggests that future research can deeply explore the performance of more factors in different market environments.

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved Q-Learning (IAC-IQL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and Q-Learning (QL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the Q-matrix in the QL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IQL algorithm were verified. The experimental results demonstrated that the IAC-IQL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of QL algorithm. SAR robots equipped with the improved IAC-IQL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

Modeling volatility in dynamic term structure models

We propose no-arbitrage term structure models with volatility factors that follow GARCH processes. The models’ tractability is similar to canonical affine term structure models, but they fit yield volatility much better, especially for long-maturity yields. This improvement does not come at the expense of a deterioration in yield fit. Because of the improved volatility fit, the model performs substantially better in pricing Treasury futures options. We conclude that the specification of the volatility factors is critical. Modeling volatility as a function of (lagged) squared innovations to factors improves on models where volatility is a linear function of the factors.

Persistent and transient variance components in option pricing models with variance-dependent Kernel

This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data.

A practical path planning method for optimal repair paths between multiple small-size defects

PurposeThis study aims to solve the problem of repair path planning between multiple small-size defects in the field of additive manufacturing (AM) repair by using Python-based ant colony algorithm (ACO). The optimal parameter combination scheme is obtained by discussing the influencing factors of parameters in the ACO.Design/methodology/approachThe effects of the information heuristic factor α, the expected heuristic factor ß and the pheromone volatile factor ρ on the simulation results were investigated by designing a three-factor and three-level orthogonal experiment. The fast convergence of ACO in finding the optimal solution of multiple small-size defect repair path problem is proved by comparing the simulation results with those of genetic algorithm (GA) on the same data set.FindingsThe ACO can effectively solve the repair path planning problem between multiple small-size defects by optimizing the parameters. In the case of 50 defect locations, the simulation results of the ACO with optimized parameters are 159.8 iterations and 3,688 average path lengths, while the GA has 4,027.2 average path lengths under the same data set and the same number of iterations, and by comparison, it is proved that the ACO can find the optimal solution quickly in the small-size defects repair path planning problem, which greatly improves the efficiency of defect repair.Originality/valueThe parameter-optimized ACO can be quickly applied to the planning problem of repair paths between multiple small-size defects in the field of AM repair, which can better improve the defect repair efficiency and reduce the waste of resources.

Export Instability, Investment, Export Growth and Economic Growth in India

The “Export-led Growth Hypothesis,” as it is known in the literature, is still a popular issue in both theoretical and empirical research. According to the export-led growth hypothesis (ELGH), a key factor in determining growth is the expansion of exports. It asserts that growing capital and labour inputs into the economy as well as exports can contribute to a country’s overall growth. The association between economic development, export growth, export fluctuation, and real effective exchange rate (REER) in India from 2001 to 2021 is examined in this paper using a multiple linear regression analysis model. Additionally, the impacts of foreign exchange, export fluctuation on investment, and import of capital goods have also been studied. The autocorrelation is also tested using the Durbin-Watson (D-W) test. Investigation of the association between income growth and exports in the context of India reveals that the country’s exports haven’t increased significantly enough to prove the theory of export-led growth. For instance, ceteris paribus, one per cent change in exports over time is expected to result in a 0.007 per cent increase in GDP. Weaker global demand, inflation, dropping exports, and growing global trade conflict all contribute to a weaker-than-expected positive link between GDP and exports. However, the findings have also shown that one per cent rise in instability increases GDP by 5.04 per cent. Furthermore, research has indicated that export instability does contribute to import for capital goods and demonstrates the relationship between investment and export instability is positive but not statistically significant. The result for policy is that, in order to decrease the negative impacts of export fluctuations, India should concentrate on those goods in which it has a competitive edge. The primary factors of the excessive volatility and restricted growth must be fully investigated in order to mitigate instability, and inherent flaws must be addressed.

Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.

Common and idiosyncratic conditional volatility: Theory and empirical evidence from electricity prices

. We propose and develop a robust score-driven multiplicative dynamic factor model for the conditional volatility of electricity prices. This model allows us to identify both common and idiosyncratic conditional volatility factors in the spot price of electricity across key Nord Pool European markets. Both factors are extracted using a single observation-driven filter that is simple and parsimonious. The common factor is robust by construction as it averages information over the cross-section of the high-dimensional vector of electricity price changes. The idiosyncratic factors are designed to capture the erratic shocks in prices and therefore rely on a robust updating equation which allows for outliers and fat-tailed innovations. Our model is suitable for high-dimensional applications and does not necessarily suffer from the curse of dimensionality. The parsimonious nature of the observation-driven filter leads to simple computations for the estimation of parameters and the signal extraction of factors. We derive the stochastic properties of the filter and its underlying factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish the consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the filter are explored in a Monte Carlo study. Finally, we apply our model and filter to the Nord Pool spot price data and find evidence that net-import and net-export electricity markets exhibit substantially different common and idiosyncratic volatilities. In particular, we confirm that net-import markets are considerably more exposed to cross-market volatility spillovers, and we quantify the relevant degree of exposition.

Whether search directions number affects the efficiency of the path planning algorithm: Taking an improved ACO algorithm with 32 directions for example

An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm.

An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional ant colony algorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced ant colony algorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.

Path-Planning Strategy: Adaptive Ant Colony Optimization Combined with an Enhanced Dynamic Window Approach

Aiming to resolve the problems of slow convergence speed and inability to plan in real time when ant colony optimization (ACO) performs global path planning, we propose a path-planning method that improves adaptive ant colony optimization (IAACO) with the dynamic window approach (DWA). Firstly, the heuristic information function is modified, and the adaptive adjustment factor is added to speed up the algorithm’s convergence rate; secondly, elite ants and max–min ants systems are implemented to enhance the global pheromone updating process, and an adaptive pheromone volatilization factor is aimed at preventing the algorithm from enhancing its global search capabilities; then, the path optimization and withdrawal mechanism is utilized to enable smoother functioning and to avoid the deadlocks; finally, a new distance function is introduced in the evaluation function of DWA to the enhance real-time obstacle-avoidance ability. The simulation experiment results reveal that the path length of the IAACO can be shortened by 10.1% and 13.7% in contrast to the ACO. The iteration count can be decreased by 63.3% and 63.0%, respectively, leading to an enhanced optimization performance in global path planning and achieving dynamic real-time obstacle avoidance for local path planning.

Research on path planning of tea picking robot based on ant colony algorithm

Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.

Macroeconomic and financial mixed frequency factors in a big data environment

Abstract In this paper, we evaluate the predictive content of 3 new business condition indexes and uncertainty measures that are estimated using high-frequency financial and low-frequency macroeconomic time series data. More specifically, our measures are defined as latent factors that are extracted from a state space model that includes multiple different frequencies of non-parametrically estimated components of quadratic variation, as well as mixed frequency macroeconomic variables. When forecasting growth rates of various monthly financial and macroeconomic variables, use of our new mixed frequency factors is shown to result in significant improvement in predictive performance, relative to a number of benchmark models. Additionally, when used to forecast corporate yields, predictive gains associated with the use of our measures are shown to be monotonically increasing, as one moves from predicting higher to lower rated bonds. This is consistent with the existence of a natural pricing channel wherein financial risk (as measured using our volatility factors) contains more predictive information for lower grade bonds. We also find that a variety of extant risk factors including the Aruoba et al. [(2009a). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417427] business conditions index also contain marginal predictive content for the variables that we examine, although their inclusion does not reduce the usefulness of our measures.

Forecasting hourly WTI oil front monthly price volatility densities

<p>Financial commodity markets have an impact on company values and cash flows, where price movements within frequent time intervals can be both significant and random. Understanding highly frequent price movements is both important and difficult. In this paper, I measured and forecasted volatility for high-frequency (mostly twelve hours per day) WTI Oil front month price movements from 2012 to 2024 (about 40,500 observations). I created a new stochastic volatility model for extracting latent volatility using the non-linear Kalman filter. Stable and strictly ergodic hourly price series, along with the BIC optimal non-linear (generic) general method of moments model coefficients, enabled this process. The latent volatility seemed to separate into two volatility factors. One factor that was very persistent suggested slow mean reversion, and one choppy strongly mean-reverting factor. The data dependence found in the factor volatility series suggested forecasting ability. I applied classical static forecasts and three machine learning regression techniques to predict one-step-ahead volatility. The two volatility factors and one summarizing exponential factor were reported, along with several fit measures, including the root mean square error and Theil's measure of covariance.</p>

Path Optimization in Robotic Welding of Plate Heat Exchangers: An Improved Ant Colony Approach

In industrial robot welding heat exchanger plate route planning, there are drawbacks to using the traditional ant colony algorithm (ACA), including poor search efficiency and a propensity to trend toward the local optimum. To address the above issues, first of all, the article introduces the improved pheromone volatilization factor, which is adjustable based on iteration times in the ACA. Secondly, it combines the new perturbation strategy and the cross‐mutation operation, proposes an improved genetic algorithm, and fuses it into the ACA, which increases the diversity of the ACA’s path searching and improves the ability of the local and global search. Finally, the improved ACA mechanism is verified by experiments and compared with six existing path planning algorithms (including three ACA variant algorithms and three mainstream algorithms). The experimental results show that the IACAG algorithm has excellent performance in welding heat exchanger path planning and can achieve lower calculation time and iteration times under the conditions of optimal solution and zero deviation, showing comprehensive advantages in solution quality, stability, and efficiency.

Research on the Impact of Digital Currency on the Operation Mechanism of Traditional Financial Markets and Countermeasures

Abstract Both digital currencies and traditional financial markets are affected by inflation, interest rates, money supply, etc. In terms of price volatility factors, there are cross-asset linkages and risk transfer problems. In this paper, we use the DCCGARCH model to calculate the time-varying nonlinear correlations between different financial markets during different periods. Firstly, we construct a GARCH model for univariate return series and derive standardized residual estimates to determine the conditional insured value and spillover risk that financial markets encounter when the risk materializes. The linkage and shock relationships of BTC with the stock and spot gold markets over ten years are analyzed, as well as the impact of unanticipated events on financial markets. It is found that the short-term impact of digital currency price volatility on the stock market is much larger than the short-term impact of stocks on the gold market and that risks in the digital currency market may be rapidly transmitted to the stock market in the short term. Bitcoin and stocks have a dynamic correlation coefficient that fluctuates between -0.075 and 0.085, with little correlation. Stocks and Bitcoin have a positive correlation of 0.021, meaning that when the price of Bitcoin rises, it can lead to a rise in the price of stocks to some extent. The dynamic correlation coefficient between bitcoin and gold, on the other hand, fluctuates in the range of -0.275 to 0.175, which may produce large fluctuations. During the Cyprus crisis, gold and bitcoin demonstrated a significant investment transfer effect, and there was a contagion and linkage between market risk, the UK’s exit from the European Union, and the new crown epidemic. This paper provides a useful exploration of cross-market financial risk regulation and professional investor risk control.

Prediction of distributions of unit prices for real estate properties on the basis of the characteristics of PSI-processes

Real estate market price forecasting is always in the focus of interests of scientists-economists, market analysts, market participants (sellers and buyers), marketing services of building complex enterprises, analysts working for banks and insurance companies and investors. Under present day conditions, the price behavior of properties on real estate markets takes especially important meaning subject to the influence of such factors as changes in the structure of household incomes, changes in mortgage rates and their availability, dynamic changes in the macroeconomic and other external socio-economic and political type factors. However, unlike the financial and securities markets, the real estate market is always characterized by a delayed reaction to external perturbations, often up to half a year, which allows us to hope for an appropriate construction of forecasts, at least in time for the delayed reaction. Traditional autoregressive forecasting methods are characterized by rapidly increasing forecast variance, because they assume a factor of stochastic volatility. This paper proposes a model and method of forecast construction based on stochastic processes of the “Poisson random index” having a short time for reaching a stationary stable variance. The model is based on the “principle of replacements” of current prices with new ones. We analyze in detail an example of the application of the “principle of replacements” for construction of price forecasts on secondary residential real estate in St. Petersburg which is based on data of four-year observations of offer prices.