Application of Elzaki Transform Method to Market Volatility Using the Black-Scholes Model

Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Optimal Quota-Share and Excess-of-Loss Reinsurance and Investment with Heston’s Stochastic Volatility Model

An optimal quota-share and excess-of-loss reinsurance and investment problem is studied for an insurer who is allowed to invest in a risk-free asset and a risky asset.Especially the price process of the risky asset is governed by Heston's stochastic volatility(SV)model.With the objective of maximizing the expected index utility of the terminal wealth of the insurance company,by using the classical tools of stochastic optimal control,the explicit expressions for optimal strategies and optimal value functions are derived.An interesting conclusion is found that it is better to buy one reinsurance than two under the assumption of this paper.Moreover,some numerical simulations and sensitivity analysis are provided.

Impact of trading hours extensions on foreign exchange volatility:intraday evidence from the Moscow exchange

Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

In this study,we proposed a new model to improve the accuracy of fore-casting the stock market volatility pattern.The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange(Tada-wul).The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations.The proposed forecasting model combines the best maximum overlapping discrete wavelet transform(MODWT)function(Bl14)and exponential generalized autoregressive conditional heteroscedasticity(EGARCH)model.The results show the model's ability to analyze stock market data,highlight important events that contain the most volatile data,and improve forecast accuracy.The results were compared from a number of mathematical mod-els,which are the non-linear spectral model,autoregressive integrated moving aver-age(ARIMA)model and EGARCH model.The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error(MAPE),Mean absolute scaled error(MASE)and Root means squared error(RMSE).

不同栽培模式对草莓果实挥发性香气物质的影响

为探明不同栽培模式对草莓果实挥发性香气物质的影响,以红颜(HY)、天使8号(TS8H)为试验材料,设置常规土壤栽培和高架基质栽培2种模式进行栽培试验。果实成熟后,采用搅拌棒吸附萃取(SBSE)结合气相色谱-质谱(GC-MS)联用技术对草莓果实的香气成分进行测定与分析。结果表明:2个草莓品种中共检测出83种、12大类挥发性香气物质。其中,脂肪酯29种、内酯3种、芳香族酯6种、脂肪醇4种、萜烯醇5种、呋喃酮2种、脂肪酮3种、萜烯酮2种、脂肪酸6种、脂肪醛8种、萜烯6种、其他9种。香气组分中,内酯、萜烯醇、脂肪酯相对含量较高。常规土壤栽培处理红颜果实内酯、萜烯醇、脂肪酯相对含量分别为46.77%、21.46%、16.18%,天使8号分别为44.20%、25.71%、19.77%;高架基质栽培处理红颜果实内酯、萜烯醇、脂肪酯相对含量分别为51.17%、24.21%、13.10%,天使8号分别为43.59%、18.11%、16.54%。不同栽培模式下,同一品种中90%以上挥发性香气物质种类一致,主要挥发性香气物质均为γ-癸内酯、γ-十二内酯、反式橙花叔醇;高架基质栽培方式下红颜果实γ-癸内酯和天使8号果实4-甲氧基-2,5-二甲基-3(2H)-呋喃酮(DMMF)的相对含量明显高于常规土壤栽培。综上,高架基质栽培在维持草莓果实主要挥发性香气物质稳定的同时,还可以提升果实的果香、甜香味。

不同干燥方式对杜仲雄花品质特性及挥发性成分的影响

为探究干燥方式对杜仲雄花品质特性及挥发性成分的影响,选择5种不同干燥方式(真空冷冻干燥、微波真空冷冻干燥、热风干燥、远红外辐射干燥和热泵干燥)对新鲜杜仲雄花进行脱水处理。测定了杜仲雄花干制品的干燥特性与品质特性,并对干制品的挥发性成分进行了分析。结果表明,真空冷冻干燥所需时间最长,为14.1 h,能耗最高,为231.27 kJ/g,微波真空冷冻干燥效率最高,与真空冷冻干燥相比,干燥时间缩短57%,干燥能耗节省76%。与其他3种干燥方式相比,真空冷冻干燥和微波真空冷冻干燥条件下杜仲雄花干制品的总色差最小,与新鲜样品最接近,表面结构更为平整,活性成分含量最高且自由基清除能力最强。在挥发性成分检测中,干燥前后共检测出27种挥发性成分,其中微波真空冷冻干燥产品挥发性成分种类最多,为18种,是新鲜杜仲雄花挥发性成分种类的1.8倍。综合分析,微波真空冷冻干燥为杜仲雄花的适宜干燥方式,该研究为杜仲雄花的干燥及加工提供理论依据。

不同前处理工艺红桔果汁与红桔果酒挥发性风味物质分析

以万州红桔为研究对象,采用带皮和去皮方式榨汁,对浊汁进行酶解澄清处理,分别利用带皮浊汁、带皮清汁、去皮浊汁及去皮清汁酿造红桔果酒。对果酒进行感官评价,同时利用液相色相仪与顶空固相微萃取(HS-SPME)-气相色谱质谱(GC-MS)联用技术测定果酒和果汁样品中有机酸及挥发性风味物质,并基于挥发性风味物质进行主成分分析(PCA)和聚类分析(CA)。结果表明,红桔果汁主要有机酸为苹果酸和柠檬酸,红桔果酒增加了乳酸和乙酸;红桔果汁和红桔果酒中共检测出100种挥发性风味物质,主要化合物包括酯类28种、醇类19种、酚类3种、醚类2种、醛类8种、酮类10种、酸类4种、烷烃类3种及烯烃类23种;总体上,清汁发酵红桔果酒的挥发性风味物质种类较浊汁发酵多,带皮发酵果酒中的醇类和烯烃种类较去皮发酵多。PCA结果表明,不同红桔果汁和红桔果酒样品中主要挥发性风味物质为辛酸乙酯、癸酸乙酯、正己酸乙酯、月桂酸乙酯、棕榈酸乙酯、α-松油醇、苯乙醇、辛醇、香叶醇、麝香草酚、L-紫苏醛、癸醛等;聚类分析结果表明,可将样品聚集为三大类,与PCA分类结果一致。

干燥方式对铁棍山药片挥发性风味成分的影响

采用顶空固相微萃取结合气相色谱-质谱联用技术(HS-SPME-GC-MS)和电子鼻,分别测定热风干燥、微波干燥、真空干燥、真空冷冻干燥、联合干燥5种干燥方式加工的铁棍山药片的挥发性风味成分,并结合主成分分析,研究干燥过程中山药片风味的变化。结果表明,在5种干燥方式制得的山药片中,共检出95种挥发性成分,主要包括烃类、醛类、酮类、醇类、酯类、苯类和杂环化合物等。壬醛、癸醛、2,6,10-三甲基十四烷、香叶基丙酮是山药片中的主要风味物质。热风干燥、真空干燥、微波干燥所得山药片的主要挥发性物质有烃类、苯类和醛类;真空冷冻干燥、联合干燥所得山药片的主要挥发性物质有烃类、醛类和酮类。电子鼻检测结果表明,5种干燥方式制得的山药片风味存在一定差异,其中热风干燥和真空干燥所得山药片的风味在醇、部分芳香族化合物上具有相似性,真空冷冻干燥山药片与其它干燥方式山药片的风味差异主要是含氮化合物、含硫化合物。这与GC-MS分析结果一致。根据主成分分析法建立了山药片香气品质评价模型,得出联合干燥方式制得的山药片综合得分最高,香气品质最好,其次为真空冷冻干燥、微波干燥、热风干燥、真空干燥。

bHLH96的克隆及其在薄荷萜烯生物合成调控中的功能

【目的】薄荷精油是日化、医药和食品工业中的重要原料,bHLH转录因子在调控植物挥发性次生代谢产物合成中具有重要作用。探究bHLH蛋白在薄荷挥发性物质合成调控中的作用,为通过代谢或基因工程改良薄荷种质提供重要基因资源,拓展有关植物挥发性次生代谢产物合成途径与调控机制的认识。【方法】利用转录组测序和RT-qPCR技术分析bHLH96在薄荷根、茎、叶中的表达,并通过RT-PCR法克隆其全长序列,再利用DNA重组技术构建植物表达载体pBI121-bHLH96,利用农杆菌介导法在薄荷叶片中过表达,检测过表达bHLH96对薄荷叶片萜烯化合物含量和合成相关基因表达的影响。【结果】薄荷bHLH96的编码区全长861 bp,编码286个氨基酸残基。薄荷bHLH96是一个细胞核定位蛋白,植物bHLH96蛋白间的序列相似性为45.45%-73.68%,薄荷bHLH96与薰衣草LaMYC4和丹参SmbHLH94等唇形科植物bHLH蛋白的亲缘关系较近。在薄荷叶片中瞬时过表达bHLH96,显著影响15种萜烯化合物的相对含量。过表达bHLH96显著上调萜烯合成相关基因OC2、SM1、KS1、ND和EAO1的表达,显著下调NLS1、LS1和iPR的表达。【结论】薄荷bHLH96可能通过调控萜烯合成相关基因的表达,影响相关萜合成酶的活性,从而调控薄荷萜烯物质的合成。

预制烤鱼水分分布及关键挥发性风味物质分析

以即热型预制烤鱼为研究对象,采用低场-核磁共振技术(Low field-nuclear magnetic resonance,LF-NMR)、顶空固相微萃取与气相色谱质谱联用技术(Headspace-solid phase microextraction-gas chromatography mass spectrometry,HS-SPME-GC-MS)对不同电烤条件下烤鱼水分分布及挥发性风味物质进行研究。结果表明:250℃烤制20 min的烤鱼加工损失率最低,持水力最高。烤鱼中水分主要形式为不易流动水,200℃烤鱼的弛豫时间T22、T23最低,其次是250℃烤鱼,T2反演谱整体向左移动,说明水的自由度降低。250℃烤制20 min的烤鱼不易流动水峰面积占比(A22)最高,达94.03%,自由水峰面积占比(A23)最低,为1.93%,说明肌肉组织持水较优。在5组烤鱼中共鉴定出70种挥发性物质,主要为醛类、酮类、醇类及烃类等。通过相对气味活度值(Relative odor activity value,ROAV)筛选出关键挥发性风味物质分别为12、9、11、8和8种。5组样品贡献较大的风味物质为己醛、1-辛烯-3-醇、壬醛等,其中愈创木酚和4-甲基苯酚是碳烤烤鱼特有的关键风味物质。通过主成分分析,不同条件烤制的烤鱼关键挥发性风味物质差异明显。相关性分析表明,A22与异戊醛、庚醛和2,3-辛二酮呈显著正相关(P<0.05),与壬醛和愈创木酚呈显著负相关(P<0.05)。该研究结果为预制烤鱼开发提供参考。

减氮施炭对温室膜下滴灌黄瓜土壤呼吸和氮素气态损失的影响

为降低温室蔬菜过量施氮的不利影响,明确减氮施炭条件下温室膜下滴灌黄瓜土壤呼吸和氮素气态排放特征,以不覆膜不施炭(CK)为对照,设置覆膜(M)、覆膜施炭(MB)、覆膜施炭减氮(MBN_(80%))共4个处理,对覆膜条件下减氮施炭处理对黄瓜产量、耗水量、土壤养分动态、土壤呼吸、N_(2)O排放和氨挥发的影响进行探讨。结果表明:与CK相比,M可降低温室膜下滴灌黄瓜全生育期耗水量20.95%,提高水分利用效率41.03%,降低0~20 cm表层土铵态氮48.12%,降低全生育期氨挥发32.35%、N_(2)O排放量14.34%和CO_(2)排放量12.68%(<0.05)。施炭后,与CK相比,MB可降低耗水量28.37%,提高水分利用效率55.60%,降低表层土铵态氮30.0%,提升硝态氮12.37%,有机质56.28%,降低氨挥发36.68%、N_(2)O排放18.64%,但却显著增大了CO_(2)排放4.66%(p<0.05)。同M对比,MB可在M基础上,进一步提升表层土有机质和铵态氮含量,降低氨挥发,但促进了CO_(2)排放。覆膜施炭减氮20%后,与CK相比,MBN_(80%)可增产25.47%,降低耗水量32.43%,提升水分生产率72.67%,降低表层土铵态氮56.33%,增加有机质51.72%,降低氨挥发40.48%、N_(2)O排放20.79%(p<0.05)。CK全生育期全球增温潜势(global warming potential,GWP)和活性氮排放分别为13.57 t·CO_(2)-eq·hm^(-2)和6.54 kg·hm^(-2),M可显著降低GWP(14.15%)和活性氮排放(27.37%);在M基础上施炭,将进一步降低活性氮排放,但导致GWP显著增大;而在MB基础上减氮20%,可同时显著降低GWP和活性氮排放(p<0.05)。与CK相比,MBN80%在通过施炭20 t·hm^(-2),减氮20%条件下,实现增产25.47%,降低耗水量32.43%,增加有机质51.72%,增加收入17.52%,降低活性氮排放35.32%和GWP 2.28%,也可在MB的基础上,进一步实现增产,降低氨挥发,并破解M和MB处理CO_(2)排放增大的问题(p<0.05)。研究揭示了减氮施炭条件下温室膜下滴灌黄瓜土壤呼吸和氮素气态排放特征,为实现温室蔬菜节水增产固碳减排提供理论依据和技术支撑。

3种不同熟化方式对彩色马铃薯挥发性风味物质组成的影响

采用气相色谱-离子迁移谱联用仪(GC-IMS)分析在不同熟化方式下彩色马铃薯的挥发性风味物质组成。结果表明:从3种不同熟化方式的彩色马铃薯样品中定性鉴定出挥发性有机物质共有52种,其中醛类物质的种类和相对含量均最高,表明醛类物质对马铃薯香味的呈现起主要作用。不同熟化方式对彩色马铃薯挥发性物质的组成及含量存在一定的差异,且不同颜色的马铃薯在同一种熟化方式下,其挥发性物质含量也表现出差异。在蒸制的彩色马铃薯中挥发性风味物质含量较低,在烤制和油炸的彩色马铃薯中风味物质含量较高。挥发性成分的种类和含量可以作为区分彩色马铃薯不同熟化方式的重要特征指标。

两种产地藏羊肉挥发性风味物质和脂肪酸组成比较研究

为评定不同地区藏羊肉的挥发性风味物质和脂肪酸组成,选取甘肃省甘南藏族自治州和青海省海北藏族自治州的藏羊各20只,分别取背最长肌进行肉品质、脂肪酸以及挥发性成分组成、感官评价等指标的测定。结果表明,两种产地的藏羊宰后pH45min和pH24h均在正常范围内,且二者之间无显著性差异;甘南藏羊的色泽和嫩度优于海北藏羊;共检测出18种脂肪酸,其中包括7种饱和脂肪酸和11种不饱和脂肪酸。两种产地藏羊肉的脂肪酸组成和含量均存在显著差异,甘南藏羊脂肪酸组成比例更佳,ω-6/ω-3接近1∶1,显著低于海北藏羊(P<0.05)。共检测出12类70种挥发性风味物质,气味活性值(OAV)分析结果表明,甘南藏羊肉比海北藏羊肉具有更丰富的肉脂香味和果香味。综上,甘南藏羊肉的脂肪酸和挥发性成分组成都更加优异,感官评分更高。

pH值调控方法对剩余污泥与柑橘废渣厌氧共发酵产酸影响

考察不同pH值调控方法对剩余污泥和柑橘废渣共发酵系统的产酸性能影响,结果表明:持续调节pH值为6可以提高共发酵系统的增溶过程,促进糖类物质水解,增强产酸和产甲烷过程,VFA产量和累积甲烷产量分别是空白组的1.36倍和1.25倍;提高共发酵系统中水解细菌和发酵产酸细菌的菌群丰度,促使甲烷生成途径从乙酰分解途径转向氢化营养途径.调节初始pH值为10可以有效促进共发酵系统中溶解性有机物的释放和达到快速水解的目的,促进产酸性能,但对产甲烷过程有抑制作用.

衍生品使用对股价波动性的影响——基于中国上市公司的实证研究

理论上,使用衍生品进行套期保值,对冲经营上面临的汇率、大宗商品价格等的价格波动风险,有助于公司的持续稳定经营,从而实现股票价格的稳定性。然而,衍生品的不当使用也会带来额外风险,冲击公司的经营,导致股票价格波动加剧。随着我国衍生品市场的发展及制度法规的落实完善,利用衍生品工具进行套期保值的上市公司群体越来越大。本文以A股上市公司为样本,利用双向固定效应模型研究了上市公司衍生品使用对股价波动率的影响。结果显示上市公司使用衍生品能够显著降低股价波动率,主要是通过降低特质波动率实现的。