Jump activity analysis for affine jump-diffusion models: Evidence from the commodity market

Fonseca, José Da; Ignatieva, Katja

doi:10.1016/j.jbankfin.2018.11.014

Cited by 25 publications

(24 citation statements)

References 26 publications

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“…Copula models have been widely used to describe the dependence between financial markets (e.g., Fonseca and Ignatieva, 2019 ; Fenech and Vosgha, 2019 ; Ouyang and Zhang, 2020 ). With a copula model, any multivariate distribution can be estimated by separately estimating the marginal distributions and the copula function.…”

Section: Introductionmentioning

confidence: 99%

Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory

Wang

Xun-hong

2021

Economic Modelling

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We propose a new approach to the study of financial contagion and contagion channels in the forex market by using a dynamic mixture copula-extreme value theory (DMC-EVT) model. This method allows us to elucidate the complex and dynamic dependence between forex markets. By analyzing 39 currencies that are actively traded on the forex market during the period 2005-2009, our empirical study shows that the DMC-EVT model outperforms the alternative copula models. Furthermore, we confirm the existence of financial contagion in the forex market during the 2007-2009 global financial crisis, and find that wealth constraints are the contagion channel during the crisis. Our results provide important insights on portfolio and risk management.

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Section: Introductionmentioning

confidence: 99%

Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory

Wang

Xun-hong

2021

Economic Modelling

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“…In this paper, we consider three typical models. The first model is the stochastic volatility model with contemporaneous jumps in returns and volatility (SVCJ), which is the most popular affine model in the literature, for example, Bakshi, Cao, and Chen (1997); Broadie, Chernov, and Johannes (2007); Da Fonseca and Ignatieva (2019); Duan and Yeh (2010); Eraker (2004); Eraker, Johannes, and Polson (2003); Kaeck, Rodrigues, and Seeger (2017); Lin and Chang (2010); Neuberger (2012); Neumann, Prokopczuk, and Simen (2016); Ruan and Zhang (2018); Zhu and Lian (2011, 2012); and others. Bakshi et al (1997), Broadie et al (2007), Eraker (2004), and Neumann et al (2016) document that the SVCJ model is good enough to fit options and returns data simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices

Cao

Ruan

Zhang

2020

Journal of Futures Markets

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This paper compares the information extracted from the S&P 500, CBOE VIX, and CBOE SKEW indices for the S&P 500 index option pricing. Based on our empirical analysis, VIX is a very informative index for option prices. Whether adding the SKEW or the VIX term structure can improve the option pricing performance depends on the model we choose. Roughly speaking, the VIX term structure is informative for some models, while the SKEW is very noisy and does not contain much important information for option prices. This paper also extends Zhang et al.(2017, J Futures Markets, 37, 211-237) into three typical affine models.

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“…For example, Todorov and Tauchen (2011) investigated the properties of jump occurrences in the S&P500/VIX index for the period from September 22, 2003 to December 31, 2008, and found that jumps in the volatility and price level in most cases occur together; however, Podolskij and Ziggel (2010) showed that intraday jumps in the S&P500 E‐mini futures and VIX futures for the period from January 1, 2008, to December 31, 2014, are low‐probability, high‐impact events, and cojumps between the return and volatility are relatively frequent events. Da Fonseca and Ignatieva (2019) investigated jump activity in the S&P500/VIX index, commodity markets, and equity markets for the period from July 15, 2008 to November 18, 2015, and found that the return and volatility do not jump together. As an emerging financial market, the development of China's financial market in recent decades has aroused extensive research interest.…”

Section: Introductionmentioning

confidence: 99%

Jump activity analysis of the equity index and the corresponding volatility: Evidence from the Chinese market

Chen

2021

Journal of Futures Markets

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This paper performs a nonparametric analysis of jump activity for the Chinese equities market. More precisely, we perform formal tests to decide whether the jumps in the 50 exchange‐traded fund (50ETF) and its volatility occur together by using the implied volatility index (iVIX) as a proxy for volatility. Our empirical findings are as follows: (i) joint jumps in the 50ETF and iVIX hardly occur, especially during noncrisis periods; (ii) there is a strong degree of dependence between the jump sizes of the 50ETF and iVIX when disaggregating jumps into their positive and negative parts; (iii) the jump component seems to contribute more to the leverage effect than the diffusive component.

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Jump activity analysis for affine jump-diffusion models: Evidence from the commodity market

Cited by 25 publications

References 26 publications

Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory

Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory

Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices

Jump activity analysis of the equity index and the corresponding volatility: Evidence from the Chinese market

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