The Memory of Stock Return Volatility: Asset Pricing Implications

Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Sibbertsen, Philipp

doi:10.2139/ssrn.3074550

Cited by 3 publications

(3 citation statements)

References 80 publications

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“…Asset pricing theories have been divided into two categories depending on the assumed contextual framework: asset pricing theories of rational expectations and asset pricing theories of irrational expectations [3]. The former implicitly assumes that there are rational investors in the market who make rational investment decisions based on market changes, that asset prices fluctuate around fundamentals, and that whenever asset prices deviate from fundamentals, the market has a mechanism to automatically adjust them back to fundamentals [4]- [5] The latter implies that there are irrational investors in the market who may be "herding" or "overvolatile" that market fundamentals no longer determine asset prices and that there are behavioral biases, market frictions, and even mispricing in the market that lead to asset price bubbles [6]- [7].…”

Section: Introductionmentioning

confidence: 99%

Simulation analysis of asset pricing based on the Gaussian process

Nong

2023

Applied Mathematics and Nonlinear Sciences

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This paper simulates and analyzes asset pricing based on the Gaussian model, simulates stock price paths under the Heston model, B-S model, and Gaussian model using the Monte Carlo simulation method, and compares with the real path. The difference between the Heston model, the B-S model, and the real value for predicting KIA financial assets is greater than 0.6, while the difference between the Gaussian model and the real value is less than 0.25. The difference between the Heston model, the B-S model, and the true value of Shenzhen Holdings’ financial assets is greater than 1, while the difference between the Gaussian model and the true value is less than 0.35. Similarly, the difference between the Heston model, the B-S model, and the true value of Shun Tak Group’s financial assets is almost greater than 0.60, while the difference between the Gaussian model and the true value is less than 0.35. It can be seen that the Gaussian model is closer to the true value than the Heston model and the B-S model, so it is reasonable and effective to use the Gaussian model to price financial assets. This study not only presents a new basis for theoretically studying financial asset pricing but also provides a theoretical basis for applying option pricing in risk management.

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Section: Introductionmentioning

confidence: 99%

Simulation analysis of asset pricing based on the Gaussian process

Nong

2023

Applied Mathematics and Nonlinear Sciences

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“…Long-memory phenomena have been observed in numerous scientific fields such as hydrology, geophysics, economics, finance, climatology, physics, biology, medicine, music, and telecommunications engineering among others (see Hurst [23], Hosking [20], Lo [28], Willinger et al [40], Baillie [1], Lai et al [26], Hu and Øksendal [22], Granger and Hyung [19], Fleming and Kirby [18], Chronopoulou and Viens [11], Rossi and Fantazzini [35], Nguyen et al [32] and the references therein). A time series with a long-memory behavior has a slow and hyperbolically declining autocorrelation function or, equivalently, an infinite spectrum at zero frequency.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for discretized geometric fractional Brownian motions with applications in Chinese financial markets

Sun

Chen

2022

Adv Cont Discr Mod

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It is widely accepted that financial data exhibit a long-memory property or a long-range dependence. In a continuous-time situation, the geometric fractional Brownian motion is an important model to characterize the long-memory property in finance. This paper thus considers the problem to estimate all unknown parameters in geometric fractional Brownian processes based on discrete observations. The estimation procedure is built upon the marriage between the bipower variation and the least-squares estimation. However, unlike the commonly used approximation of the likelihood and transition density methods, we do not require a small sampling interval. The strong consistency of these proposed estimators can be established as the sample size increases to infinity in a chosen sampling interval. A simulation study is also conducted to assess the performance of the derived method by comparing with two existing approaches proposed by Misiran et al. (International Conference on Optimization and Control 2010, pp. 573–586, 2010) and Xiao et al. (J. Stat. Comput. Simul. 85(2):269–283, 2015), respectively. Finally, we apply the proposed estimation approach in the analysis of Chinese financial markets to show the potential applications in realistic contexts.

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“…We contribute to the existing literature by largely extending the sample of countries to eightytwo and examining the cross-sectional variation of long memory across countries and its link to macroeconomic variables. Nguyen et al (2017) investigate the cross-sectional variation of long memory in volatility at the firm level. They provide evidence of long memory in volatility for the cross-section of U.S. stocks and find a negative price for long memory volatility.…”

Section: Introductionmentioning

confidence: 99%

The Long Memory of Equity Volatility: International Evidence

et al. 2017

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The Memory of Stock Return Volatility: Asset Pricing Implications

Cited by 3 publications

References 80 publications

Simulation analysis of asset pricing based on the Gaussian process

Simulation analysis of asset pricing based on the Gaussian process

Parameter estimation for discretized geometric fractional Brownian motions with applications in Chinese financial markets

The Long Memory of Equity Volatility: International Evidence

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