scholarly journals Pricing European options on agriculture commodity prices using mean-reversion model with jump diffusion

2017 ◽  
Author(s):  
Komang Dharmawan
Keyword(s):  
Commodity Prices ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
European Options

Noise trading and stock market bubbles: what the derivatives market is telling us

2020 ◽  
Vol 46 (9) ◽  
pp. 1165-1182
Author(s):  
Scott B. Beyer ◽  
J. Christopher Hughen ◽  
Robert A. Kunkel
Keyword(s):  
Stochastic Volatility ◽  
Noise Level ◽  
Implied Volatility ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Futures Prices ◽  
Noise Traders ◽  
Content Type ◽  
Noise Trading ◽  
Technology Bubble

PurposeThe authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.Design/methodology/approachTo understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.FindingsIn the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.Research limitations/implicationsThis study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.Practical implicationsBloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.Originality/valueThis paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.

A theoretical model of jump diffusion-mean reversion

2017 ◽  
Vol 23 (3) ◽  
pp. 537-554
Author(s):  
Anindya Chakrabarty ◽  
Zongwei Luo ◽  
Rameshwar Dubey ◽  
Shan Jiang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Theoretical Model ◽  
Interest Rate ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Fixed Income ◽  
Short Term ◽  
Content Type ◽  
Better Than

Purpose The purpose of this paper is to develop a theoretical model of a jump diffusion-mean reversion constant proportion portfolio insurance strategy under the presence of transaction cost and stochastic floor as opposed to the deterministic floor used in the previous literatures. Design/methodology/approach The paper adopts Merton’s jump diffusion (JD) model to simulate the price path followed by risky assets and the CIR mean reversion model to simulate the path followed by the short-term interest rate. The floor of the CPPI strategy is linked to the stochastic process driving the value of a fixed income instrument whose yield follows the CIR mean reversion model. The developed model is benchmarked against CNX-NIFTY 50 and is back tested during the extreme regimes in the Indian market using the scenario-based Monte Carlo simulation technique. Findings Back testing the algorithm using Monte Carlo simulation across the crisis and recovery phases of the 2008 recession regime revealed that the portfolio performs better than the risky markets during the crisis by hedging the downside risk effectively and performs better than the fixed income instruments during the growth phase by leveraging on the upside potential. This makes it a value-enhancing proposition for the risk-averse investors. Originality/value The study modifies the CPPI algorithm by re-defining the floor of the algorithm to be a stochastic mean reverting process which is guided by the movement of the short-term interest rate in the economy. This development is more relevant for two reasons: first, the short-term interest rate changes with time, and hence the constant yield during each rebalancing steps is not practically feasible; second, the historical literatures have revealed that the short-term interest rate tends to move opposite to that of the equity market. Thereby, during the bear run the floor will increase at a higher rate, whereas the growth of the floor will stagnate during the bull phase which aids the model to capitalize on the upward potential during the growth phase and to cut down on the exposure during the crisis phase.

The multiple-mean-reversion jump-diffusion model for Nordic electricity spot prices

2011 ◽  
Vol 4 (2) ◽  
pp. 3-25 ◽  
Author(s):  
Matylda Jabłońska ◽  
Hasifa Nampala ◽  
Tuomo Kauranne
Keyword(s):  
Diffusion Model ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Spot Prices ◽  
Jump Diffusion Model

scholarly journals Bubble Troubles? Rational Storage, Mean Reversion and Runs in Commodity Prices.

2013 ◽  
Author(s):  
Eugenio S. Bobenrieth ◽  
Juan R. Bobenrieth ◽  
Brian Wright
Keyword(s):  
Commodity Prices ◽  
Mean Reversion

NUMERICAL SCHEMES FOR OPTION PRICING IN REGIME-SWITCHING JUMP DIFFUSION MODELS

2013 ◽  
Vol 16 (08) ◽  
pp. 1350046 ◽  
Author(s):  
IONUT FLORESCU ◽  
RUIHUA LIU ◽  
MARIA CRISTINA MARIANI ◽  
GRANVILLE SEWELL
Keyword(s):  
Complex System ◽  
Regime Switching ◽  
Traditional Approach ◽  
Parabolic Type ◽  
Jump Diffusion ◽  
European Options ◽  
Jump Diffusion Model ◽  
Proof Of Convergence ◽  
New Algorithms ◽  
Signal Variability

In this paper, we present algorithms to solve a complex system of partial integro-differential equations (PIDE's) of parabolic type. The system is motivated by applications in finance where the solution of the system gives the price of European options in a regime-switching jump diffusion model. The new algorithms are based on theoretical analysis in Florescu et al. (2012) where the proof of convergence of the algorithms is carried out. The problems are also solved using a more traditional approach, where the integral terms (but not the derivative terms) are treated explicitly. Another contribution of this work details a novel type of jump distribution. Empirical evidence suggests that this type of distribution may be more appropriate to model jumps as it makes them more clearly distinguishable from the signal variability.

The double exponential jump diffusion model for pricing European options under fuzzy environments

2012 ◽  
Vol 29 (3) ◽  
pp. 780-786 ◽  
Author(s):  
Li-Hua Zhang ◽  
Wei-Guo Zhang ◽  
Wei-Jun Xu ◽  
Wei-Lin Xiao
Keyword(s):  
Diffusion Model ◽  
Jump Diffusion ◽  
European Options ◽  
Double Exponential ◽  
Jump Diffusion Model
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