“…When fC 1 ; C 2 g ¼ f0; 1g, the price dynamic is a MRSVJ process similar to Pillay and O'Hara (2011). The jumps in the model handle abrupt changes in the spot log price that happen due to supply and demand shocks in the commodity market.…”
Section: The General-form Affine Styled-facts Dynamicsmentioning
confidence: 98%
“…For this study, we specify a general form for affine styled-fact price dynamics that allows for mean reversion dynamics of Wong andLo (2009), Pillay andO'Hara (2011) and seasonality.…”
Section: Preliminariesmentioning
confidence: 99%
“…Weron (2006) and Mayer et al (2015) look at seasonality. Pillay and O'Hara (2011) discuss MRSV model with jump risk. Cortazar et al (2015) documents that, although the prior researches on commodity market include several desirable properties, little attention has been put on the performance 1 Heston (1993) provides a closed form solution based on SV price dynamics.…”
This study analyzes affine styled-facts price dynamics of Henry Hub natural gas price by incorporating the price features of jump risk, and seasonality within stochastic volatility framework. Affine styled-facts dynamics has the advantage of being able to incorporate mean reversion (MR), stochastic volatility (SV), seasonality trends (S), and jump diffusion (J) in a standardized inclusive framework. Our main finding is that models that incorporate jumps significantly improve overall out-of-sample option pricing performance. The combined MRSVJS model provides the best fit of both daily gas price returns and the related cross section of option prices. Incorporating seasonal effects tend to provide more stable pricing ability, especially for the long-term option contracts.
“…When fC 1 ; C 2 g ¼ f0; 1g, the price dynamic is a MRSVJ process similar to Pillay and O'Hara (2011). The jumps in the model handle abrupt changes in the spot log price that happen due to supply and demand shocks in the commodity market.…”
Section: The General-form Affine Styled-facts Dynamicsmentioning
confidence: 98%
“…For this study, we specify a general form for affine styled-fact price dynamics that allows for mean reversion dynamics of Wong andLo (2009), Pillay andO'Hara (2011) and seasonality.…”
Section: Preliminariesmentioning
confidence: 99%
“…Weron (2006) and Mayer et al (2015) look at seasonality. Pillay and O'Hara (2011) discuss MRSV model with jump risk. Cortazar et al (2015) documents that, although the prior researches on commodity market include several desirable properties, little attention has been put on the performance 1 Heston (1993) provides a closed form solution based on SV price dynamics.…”
This study analyzes affine styled-facts price dynamics of Henry Hub natural gas price by incorporating the price features of jump risk, and seasonality within stochastic volatility framework. Affine styled-facts dynamics has the advantage of being able to incorporate mean reversion (MR), stochastic volatility (SV), seasonality trends (S), and jump diffusion (J) in a standardized inclusive framework. Our main finding is that models that incorporate jumps significantly improve overall out-of-sample option pricing performance. The combined MRSVJS model provides the best fit of both daily gas price returns and the related cross section of option prices. Incorporating seasonal effects tend to provide more stable pricing ability, especially for the long-term option contracts.
“…Elliot, Sui and Chan [17] present change in volatility as a switching Markov process with the transition accomplished through an Esscher transformation. Both Thavaneswaran and Singh (TS) [18,19] and Pillay and O'Hara's [20] (PH) incorporate the lognormal distribution. TS has a jump diffusion model with stochastic volatility, where the expiration price is a moment of a truncated log-normal distribution and PH has the stock price follow a mean reverting log-normal process with stochastic diffusion and jumps and the option's price determine by fast Fourier transformation methodology.…”
This paper presents a new option that can be used by agents for managing foreign exchange risk. Unlike the Garman Kolhagen model [1], (GK), this paper presents a new model with a preset exchange rate (PE), that allows the agent to take advantage of the his/her view on both the direction and magnitude of rate movement and as such provides this agent with more choices. The model has a provision for an automatic exchange of the payoff at a preset exchange rate, and upon expiration gives the agent the choice of keeping the payoff in the foreign currency or exchanging it back to the pricing currency. At the time of writing, the buyer selects the preset exchange rate. Depending on the value selected, the PE option's price and payoff will be equal to, greater than or less than those of the GK model. A decision rule for choosing between the PE and GK models is developed by determining the expiration spot rate that equates the two models' returns. The range of spot rates that makes the PE option's return greater than the GK's return is the PE preferred range. If the agent expects the expiration spot rate will be within the preferred range, the PE option is purchased. The size of the preferred range is a decreasing function of time to expiration, a decreasing function of spot rate volatility and an increasing function of the basis point spread between foreign and domestic interest rates.
“…In general, it is an asset model, which shows that the asset price tends to fall (or rise) after hitting a maximum (or minimum). The process of mean reversion is a lognormal diffusion, but the variance does not growing in proportion to the time interval (Pillay and O'Hare, 2011). The variance grows at the start and sometimes it stabilises at a certain value.…”
Section: Background On Mean Reversion and Coefficient Of Variancementioning
Inaccuracy of a kernel function used in Support Vector Machine (SVM) can be found when simulated with nonlinear and stationary datasets. To minimise the error, we propose a new multiclass SVM model using mean reversion and coefficient of variance algorithm to partition and classify imbalance in datasets. By introducing a series of test statistic, simulations of the proposed algorithm outperformed the performance of the SVM model without using multiclass SVM model.
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