17 results for “Volatility Models”

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Research ArticleOpen accessIn the abstractDrawdown and Drawup of Bi-Directional Grid Constrained Stochastic ProcessesAldo Taranto, Shahjahan KhanThe Grid Trading Problem (GTP) of mathematical finance, used in portfolio loss minimization, generalized dynamic hedging and algorithmic trading, is researched by examining the impact of the drawdown and drawup of discrete random walks and of Itô diffusions on the Bi-Directional Grid Constrained (BGC) stochastic process for profit Pt and equity Et over time. A comprehensive Discrete Difference Equation (DDE) … …Journal of Mathematics and Statistics, Vol. 16, Issue 1 · 14 Sep 2020 · 9,153 accessesView PDF Cite

Research ArticleOpen accessIn the abstractModel Comparison for the Prediction of Stock Prices in the NYSEVictoria Switlyk, Junfeng ShangThe stock market is an integral part of investments as well as the economy. The prediction of stock prices is an exciting and challenging problem that has been considered by many due to the complexity and noise within the market and to the potential profit that can be yielded from accurate predictions. We aim to construct and compare models used … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 26 Sep 2019 · 7,475 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Hybrid Artificial Neural Network Model for Option PricingHanningtone Meshack Simiyu, Anthony Gichuhi Waititu, Jane Aduda AkinyiIn the absence of a well-defined input selection technique associated with the pure ANN models, Option pricing using pure ANN models while relaxing the assumption of constant volatility remains a challenge. The conservative drill espoused has been to make allowances for a large number of input lags with the confidence that the ability of ANN to integrate suppleness and redundancy … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 8 Aug 2019 · 7,410 accessesView PDF Cite

Research ArticleOpen accessIn the abstractComparative Analysis of the Artificial Neural Networks Options Pricing Model Under Constant and Time-Variant VolatilitiesHanningtone Meshack Simiyu, Anthony Gichuhi Waititu, Jane Aduda AkinyiOption pricing using artificial neural networks (ANN) model while relaxing the assumption of constant volatility still remains a challenge. The conventional practice for pure ANN models has been to either model volatility using the very ANN model and have the model output fed as an input to the ANN option pricing model, or to make allowances for a large number … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 20 Jul 2019 · 7,010 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Review of the Recent Advances Made in the Black-Scholes Models and Respective Solutions MethodsYasir Hamad Al Saedi, Gurudeo Anand TularamThe Black-Scholes model has been a major advance in finance over a period of time; this paper examines this model in some detail, in terms of the latest developments in both analytical and numerical solutions. The paper initially briefly examines historical aspects but quickly moves to a critical analysis of the works in the applications, solutions and implications for the … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 8 Jan 2018 · 8,621 accessesView PDF Cite

Research ArticleOpen accessIn the abstractAre There Smaller Leverage Effects in Less-Developed Markets? Evidence from an Oil Exporting CountryMosayeb Phalavani, Parinaz EzzatiProblem statement: This study uses daily data from the Tehran Stock Market (TSM) to illustrate the nature of stock market volatility in an undeveloped and young stock market. Although most studies suggest that a negative shock to stock prices will generate more volatility than a positive shock of equal magnitude but there is no evidence of asymmetric effect in TSM. … …American Journal of Applied Sciences, Vol. 7, Issue 1 · 31 Jan 2010 · 7,519 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Generalized Discrete-Time Long Memory Volatility Model for Financial Stock ExchangeChin Wen CheongWe proposed a simple way to combine a few long memory models in financial market volatility modeling using daily, range and high frequency data. This model was able to fit the return, range of daily return or realized volatility under a parametric heavy-tailed distribution. Model was flexible to include additional volatility information as the contemporaneous variables. Empirical results found that … …American Journal of Applied Sciences, Vol. 4, Issue 12 · 31 Dec 2007 · 6,869 accessesView PDF Cite