First Exit-Time Analysis for an Approximate Barndorff-Nielsen and Shephard Model with Stationary Self-Decomposable Variance Process

Awasthi, Shantanu; Sengupta, Indranil

doi:10.31390/josa.2.1.05

Cited by 5 publications

(7 citation statements)

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“…In the plots in Figures 6-9, we provide the histograms corresponding to the first-exit time of the variables "exrate arg," "Gulf duenext10d," "gulf loaded post 7 days," and "Wtime santos," respectively, for the empirical dataset for various values of t. Along with the histograms, we use Gamma-type subordinators (Z) described with Lévy density w(x) = 𝜈𝛼e −𝛼x . After finding appropriate parameter values, in those plots, we plot the probability density functions of inf {s > 0 ∶ Z s ≥ t}, for various values of t. This is motivated by Theorem 2.5, and the analysis in [1]. In Figure 6 we use t = 1,2,4, and in Figure 7 we use t = 1,2,3,4.…”

Section: Results From Rfmentioning

confidence: 99%

“…It is typically very useful in understanding various financial sectors, especially the insurance industry and investment firms. (see [1]). Consequently, at this point, we incorporate Theorem 2.5 in the empirical data analysis.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In addition to that, 𝜎 t is assumed to be stochastic, and its dynamics is governed by (1) t H (1) t , 𝛽 (2) t H (2) t , … , 𝛽…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…It is clear that a single jump term for "'big" fluctuations is very unlikely. Consequently, we propose to use the model as given by Equation (1).…”

Section: Mathematical Formulationmentioning

confidence: 99%

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Machine learning and neural network based model predictions of soybean export shares from US Gulf to China

Awasthi

Sengupta

Wilson

et al. 2022

Statistical Analysis

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In this paper, we propose a general model for the soybean export market share dynamics and provide several theoretical analyses related to a special case of the general model. We implement machine and neural network algorithms to train, analyze, and predict US Gulf soybean market shares (target variable) to China using weekly time series data consisting of several features between January 6, 2012 and January 3, 2020. Overall, the results indicate that US Gulf soybean market shares to China are volatile and can be effectively explained (predicted) using a set of logical input variables. Some of the variables, including shipments due at US Gulf port in 10 days, cost of transporting soybean shipments via barge at Mid-Mississippi, and soybean exports loaded at US Gulf port in the past 7 days, and binary variables have shown significant influence in predicting soybean market shares.

show abstract

Section: Results From Rfmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

“…In addition to that, 𝜎 t is assumed to be stochastic, and its dynamics is governed by (1) t H (1) t , 𝛽 (2) t H (2) t , … , 𝛽…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…It is clear that a single jump term for "'big" fluctuations is very unlikely. Consequently, we propose to use the model as given by Equation (1).…”

Section: Mathematical Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning and neural network based model predictions of soybean export shares from US Gulf to China

Awasthi

Sengupta

Wilson

et al. 2022

Statistical Analysis

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show abstract

“…In most of the researches on price volatility, stochastic volatility models are constructed by using deterministic information, and the fact that there are a lot of fuzzy and uncertain information in the process of price change caused by various emergencies is ignored [11,13]. In the study of dynamic volatility in an uncertain market environment, the classical BN-S models containing a single Ornstein-Uhlenbeck (OU) process [35] often fails due to lack of long-range dependence in practical application [3]. There are few studies about volatility estimation in fuzzy random uncertain environment.…”

Section: Introductionmentioning

confidence: 99%

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

Hui¹,

Sun²,

Zhou³

2022

Preprint

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Predicting the dynamic volatility in financial market provides a promising method for risk prediction, asset pricing and market supervision. Barndorff-Nielsen and Shephard model (BN-S) model, used to capture the stochastic behavior of high-frequency time series, is an accepted stochastic volatility model with Lévy process. Although this model is attractive and successful in theory, it needs to be improved in application. We build a new generalized BN-S model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Calculation results show that new model outperforms the classic model in volatility forecasting. Experiments on Dow Jones Industrial Average futures price data are conducted to verify feasibility and practicability of our proposed approach. Numerical examples are provided to illustrate the theoretical result. Three machine learning algorithms are applied to estimate new model parameter. Compared with the classical model, our method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility more flexible and has ideal performance.

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Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters

Hui

Sun

Jiang

et al. 2022

Stochastic Analysis and Applications

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First Exit-Time Analysis for an Approximate Barndorff-Nielsen and Shephard Model with Stationary Self-Decomposable Variance Process

Cited by 5 publications

References 0 publications

Machine learning and neural network based model predictions of soybean export shares from US Gulf to China

Machine learning and neural network based model predictions of soybean export shares from US Gulf to China

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters

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