This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><...This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of </span><span style="font-family:Verdana;">k-serial tenures (ability to handle cycles), Markov transitional mixing weights</span><span style="font-family:Verdana;">, switching of mingling autoregressive processes and full range shape changing </span><span style="font-family:Verdana;">predictive distributions (multimodalities) that are usually caused by large fluctuation</span><span style="font-family:Verdana;">s (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">fitted among the Poisson and Extreme-Value-</span><span style="font-family:Verdana;">Distributions Mingled autoregressive models subjected to the discrete monthly</span><span style="font-family:Verdana;"> stocks sold series.展开更多
依托中国气象局雷电野外科学试验基地人工引雷试验平台,利用自主研发的探测设备对发生于2023年7月18日的一次负极性人工触发闪电进行综合观测,获取了2次箭式先导-回击过程(RS1和RS2)高时间分辨、多参量互补的闪电电流、电场、磁场、甚高...依托中国气象局雷电野外科学试验基地人工引雷试验平台,利用自主研发的探测设备对发生于2023年7月18日的一次负极性人工触发闪电进行综合观测,获取了2次箭式先导-回击过程(RS1和RS2)高时间分辨、多参量互补的闪电电流、电场、磁场、甚高频(very high frequency,VHF)辐射以及X射线脉冲数据序列。结果表明:两次回击过程的下行先导进入气化钢丝通道及接地引起回击瞬间产生强烈的VHF和高能量X射线辐射,但其箭式先导发展速率存在较大差异,RS1先导发展速率达到10^(7) m·s^(-1)量级,对应较强的VHF辐射和连续型X射线脉冲;RS2先导发展速率达到10^(6) m·s^(-1)量级,相对于RS1二维发展速率低1个量级,VHF辐射和X射线脉冲相对较弱,且X射线呈分立脉冲特征;高能量X射线脉冲与先导沿钢丝通道下传接地引起回击的过程在时间上吻合,指示高能量X射线与回击启动过程间的直接关联,为揭示闪电回击阶段X射线的强场辐射机制提供了重要观测依据。展开更多
A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean revers...A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.展开更多
文摘This paper expounds the nitty-gritty of stock returns transitory, periodical behavior </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of </span><span style="font-family:Verdana;">k-serial tenures (ability to handle cycles), Markov transitional mixing weights</span><span style="font-family:Verdana;">, switching of mingling autoregressive processes and full range shape changing </span><span style="font-family:Verdana;">predictive distributions (multimodalities) that are usually caused by large fluctuation</span><span style="font-family:Verdana;">s (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">fitted among the Poisson and Extreme-Value-</span><span style="font-family:Verdana;">Distributions Mingled autoregressive models subjected to the discrete monthly</span><span style="font-family:Verdana;"> stocks sold series.
文摘依托中国气象局雷电野外科学试验基地人工引雷试验平台,利用自主研发的探测设备对发生于2023年7月18日的一次负极性人工触发闪电进行综合观测,获取了2次箭式先导-回击过程(RS1和RS2)高时间分辨、多参量互补的闪电电流、电场、磁场、甚高频(very high frequency,VHF)辐射以及X射线脉冲数据序列。结果表明:两次回击过程的下行先导进入气化钢丝通道及接地引起回击瞬间产生强烈的VHF和高能量X射线辐射,但其箭式先导发展速率存在较大差异,RS1先导发展速率达到10^(7) m·s^(-1)量级,对应较强的VHF辐射和连续型X射线脉冲;RS2先导发展速率达到10^(6) m·s^(-1)量级,相对于RS1二维发展速率低1个量级,VHF辐射和X射线脉冲相对较弱,且X射线呈分立脉冲特征;高能量X射线脉冲与先导沿钢丝通道下传接地引起回击的过程在时间上吻合,指示高能量X射线与回击启动过程间的直接关联,为揭示闪电回击阶段X射线的强场辐射机制提供了重要观测依据。
文摘A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.
