An efficient method for pricing foreign currency options

Chen, Rongda; Zhou, Hanxian; Yu, Lean; Jin, Chenglu; Zhang, Shuonan

doi:10.1016/j.intfin.2021.101295

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…This is one of the most accepted models in finance. Combined with different application scenarios, the previous relevant studies have proposed effective improvements of the basic model, and carried out multi-dimensional research on the stochastic volatility process of asset price, such as jump capture 2 [11,17], fluctuation estimation [18,28], pricing [12,14,28], and risk management [4,8].…”

Section: Introductionmentioning

confidence: 99%

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

Hui¹,

Sun²,

Zhou³

2022

Preprint

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Predicting the dynamic volatility in financial market provides a promising method for risk prediction, asset pricing and market supervision. Barndorff-Nielsen and Shephard model (BN-S) model, used to capture the stochastic behavior of high-frequency time series, is an accepted stochastic volatility model with Lévy process. Although this model is attractive and successful in theory, it needs to be improved in application. We build a new generalized BN-S model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Calculation results show that new model outperforms the classic model in volatility forecasting. Experiments on Dow Jones Industrial Average futures price data are conducted to verify feasibility and practicability of our proposed approach. Numerical examples are provided to illustrate the theoretical result. Three machine learning algorithms are applied to estimate new model parameter. Compared with the classical model, our method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility more flexible and has ideal performance.

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Section: Introductionmentioning

confidence: 99%

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

Hui¹,

Sun²,

Zhou³

2022

Preprint

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“…Chen vd. [8], döviz getirilerinin ağır kuyruklu dağılımlara sahip olduğunu göz önüne alarak, döviz opsiyonlarını fiyatlandırmak ve etkin bir yöntem önermek için, dağılımın kuyruğu, Normal dağılım yerine Student-t dağılımı kullanılarak modellemişlerdir ve Student-t dağılımının parametrelerini MME ile tahmin etmişlerdir. Negri ve Nishiyama [9], MME'yi kullanan bir değişim noktası saptama prosedürü önermişlerdir.…”

Section: Introductionunclassified

On Parameter Estimation with the Method of Moments

Bulut

DEMİRALP

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Momentler Metodu, bir istatistiksel modelin parametrelerini tahmin etmek için kullanılır. Bu yöntem örnek momentleri ile anakütle momentleri arasındaki ilişki ile verilen denklemlerin çözümü ile parametrelerin değerlerini bulmayı amaçlar. Literatürde bilinen ilk tahmin yöntemi olan Momentler Metodu ilk olarak Pearson tarafından ortaya atılmıştır. Uygulanabilirliği, basit ve anlaşılır olmasından dolayı sürekli başvurulan bir yöntemdir. Bu çalışmada, Binom, Poisson, Sürekli Düzgün ve Gamma dağılımlarının bilinmeyen parametrelerinin tahmincileri Momentler Metodu ile elde edilmiş ve verilen dağılımlar için tesadüfi veriler simüle edilerek gerçek değerleri ile tahmin değerleri karşılaştırılmıştır.

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Currency portfolio behavior in seven major Asian markets

Chang

Lin

2023

Economic Analysis and Policy

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An efficient method for pricing foreign currency options

Cited by 4 publications

References 40 publications

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

On Parameter Estimation with the Method of Moments

Currency portfolio behavior in seven major Asian markets

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