Stochastic Volatility Analysis Model of regional financial market based on S-type membership function

2021 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Stochastic Volatility ◽  
Financial Market ◽  
Membership Function ◽  
Analysis Model ◽  
Volatility Analysis

scholarly journals Time-Consistent Investment and Reinsurance Strategies for Mean-Variance Insurers under Stochastic Interest Rate and Stochastic Volatility

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2183
Author(s):  
Jiaqi Zhu ◽  
Shenghong Li
Keyword(s):  
Interest Rate ◽  
Stochastic Volatility ◽  
Financial Market ◽  
Equilibrium Strategy ◽  
Stochastic Interest Rate ◽  
Programming Approach ◽  
Model Parameters ◽  
Stochastic Interest ◽  
The Impact ◽  
Mean Variance

This paper studies the time-consistent optimal investment and reinsurance problem for mean-variance insurers when considering both stochastic interest rate and stochastic volatility in the financial market. The insurers are allowed to transfer insurance risk by proportional reinsurance or acquiring new business, and the jump-diffusion process models the surplus process. The financial market consists of a risk-free asset, a bond, and a stock modelled by Heston’s stochastic volatility model. Interest rate in the market is modelled by the Vasicek model. By using extended dynamic programming approach, we explicitly derive equilibrium reinsurance-investment strategies and value functions. In addition, we provide and prove a verification theorem and then prove the solution we get satisfies it. Moreover, sensitive analysis is given to show the impact of several model parameters on equilibrium strategy and the efficient frontier.

scholarly journals Jump Volatility Estimates of High Frequency Data and Analysis Based on HHT

2015 ◽  
Vol 7 (11) ◽  
pp. 242
Author(s):  
Jiangrui Chen ◽  
Lianqian Yin ◽  
Sizhe Hou ◽  
Wei Zhang ◽  
Xiaojie Liu ◽  
...  
Keyword(s):  
Financial Markets ◽  
Capital Asset Pricing ◽  
Financial Risks ◽  
Analysis Model ◽  
Hilbert Huang Transform ◽  
Asset Pricing Model ◽  
Research Findings ◽  
Volatility Analysis ◽  
Trend Functions ◽  
Financial Market Risk

<p>As the global financial market risk increases, countries stress more onthe management and prevention of financial risks. These financial risks come from the volatility of the market, and thus we can build more comprehensive understanding of financial markets by analyzing the composition and the law of the financial volatility in different frequency. Based on Hilbert Huang Transform, the realized volatility analysis model is establishedto decompose the volatility into various signal in dissimilar frequency. First of all, the realized leap volatility is obtained through the previous research findings and Capital Asset Pricing Model. Then, considering thenonlinearity and instability of the volatility, we use the Hilbert Huang Transform to decompose the volatility and obtain IMFs in different frequencies and trend functions.</p>

A Improvement Analysis Model Based on Fuzzy Parameters Reliability

2012 ◽  
Vol 487 ◽  
pp. 764-769 ◽  
Author(s):  
Fang Liang Dong
Keyword(s):  
Reliability Analysis ◽  
Membership Function ◽  
Fuzzy Mathematics ◽  
Analysis Method ◽  
Analysis Model ◽  
Fuzzy Reliability ◽  
Cascade System ◽  
Computing Complexity ◽  
Fuzzy Parameter ◽  
Fuzzy Parameters

Fuzzy Structure; Fuzzy Parameters; Reliability; Membership Function Abstract. This paper takes the fuzzy mathematics' reliability analysis as study object, has conducted the research to the fuzzy parameter system's reliability; comparing with traditional reliability analysis, obtained more precise fuzzy reliability confidence interval formula and carried on the proving based on cascade system, which has solved the computation complexity and the low precise problem in the tradition fuzzy reliability analysis's mass fuzzy operation. Method in the paper has certain promotional value in the fuzzy problem's research. It solved some problems such as the traditional fuzzy reliability analysis in the mass caused by the calculation of fuzzy computing complexity and low-precision. The emphasis of the article is divided into two parts: One is analysis and discussion on traditional module reliability analysis method and the fuzzy number membership function; the other one is proposing one new fuzzy parameter reliability analysis method and the conclusion, based on the former analysis.

scholarly journals Determinan Harga Saham Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

2018 ◽  
Vol 8 (1) ◽  
pp. 42-54
Author(s):  
Sukma Irdiana
Keyword(s):  
High Risk ◽  
Financial Market ◽  
Capital Market ◽  
Stock Prices ◽  
Stock Price ◽  
Sample Selection ◽  
Selection Method ◽  
Coefficient Of Determination ◽  
Analysis Model ◽  
High Return

Capital market is one form of financial market, where the capital market players invest in the form of securities offered by the issuer. One of the most popular securities for sale on the stock market is stock. Stocks are securities that have high profits and risks or better known as high risk and high return. High risk and high return is the higher the risk of an investment, the higher the amount of profits derived from an investment. The purpose of this study is to obtain evidence empirically and find clarity about the influence of fundamental variables consisting of ROA, EPS, NPM, DER and BVS to stock prices of companies listed in IDX period 2010-2015, either partially or simultaneously. Sample selection method used is purposive sampling and analysis model used is multiple linier regression analysis. The results showed that partially ROA, EPS, NPM, DER, and BVS have a significant effect on stock prices. Simultaneously ROA, ROE, EPS, NPM, DER and BVS simultaneously affect the stock price. Coefficient of Determination (R Square) of 48% and the rest of 52% influenced by other variables that have not been studied in this study.

scholarly journals A Volatility Analysis of Agricultural Commodity and Crude Oil Global Markets

2017 ◽  
Vol 4 (2) ◽  
pp. 129
Author(s):  
Jamel Trabelsi ◽  
Mohamed Mehdi Jelassi ◽  
Gaye Del Lo
Keyword(s):  
Crude Oil ◽  
Financial Market ◽  
Value At Risk ◽  
Oil Prices ◽  
Commodity Markets ◽  
Global Markets ◽  
Commodity Market ◽  
Agricultural Commodity ◽  
Oil Markets ◽  
Volatility Analysis

The purpose of this study is to provide insights on volatility features of major agricultural commodity global markets. In order to achieve this, we estimate the volatility in the global markets of crude oil and four main agricultural commodities, namely rice, wheat, cotton and coffee over the period 1980:2014. We also investigate the nexus between the volatilities in these global markets. More precisely, we first model the volatility of agricultural commodity and crude oil markets based on the GARCH methodology. Second, we assess the risk in these global markets by the Value-at-Risk technique. Finally, we evaluate the co-movements between returns in agricultural commodity and crude oil markets by the copula methodology. Our empirical findings reveal that, unlike in the financial market, upside shocks in the agricultural market tend to increase volatility more than downside shocks do. In addition to that, risk in global agricultural commodity markets turned out to be high and little evidence in favor of interdependence between these markets is found. Moreover, the co-movement between agricultural commodity market risk and oil prices is detected for recent years only and little evidence is found for the whole sample period.

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