Self-Exciting Jumps, Learning, and Asset Pricing Implications

Fülöp, András; Li, Junye; Yu, Jun

doi:10.1093/rfs/hhu078

Cited by 87 publications

(34 citation statements)

References 37 publications

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“…For example, Liu, Longstaff, and Pan () highlight the importance of recognizing volatility jumps for optimal portfolio allocation. Fulop, Li, and Yu () develop a self‐exciting asset‐pricing model that accounts for price and volatility jumps and show that the model has crucial implications for volatility forecasting and option pricing.…”

Section: Introductionmentioning

confidence: 99%

Volatility jumps and macroeconomic news announcements

Chan

Gray

2018

Journal of Futures Markets

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While prior literature documents a link between macroeconomic news and price jumps, this paper demonstrates two channels through which economic announcements also manifest in volatility jumps. First, there is a strong coincidence of volatility jumps with scheduled announcements. Second, the mean jump size is an asymmetric function of the news surprise, with bad news resulting in larger jumps than good news. Furthermore, realized volatility (RV) and option‐implied volatility (IV) behave very differently over the days surrounding announcements. RV increases sharply on announcement days, while IV tends to decline consistent with the resolution of heightened uncertainty embedded in option prices.

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Section: Introductionmentioning

confidence: 99%

Volatility jumps and macroeconomic news announcements

Chan

Gray

2018

Journal of Futures Markets

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“…Advances in Bayesian estimation methods and stochastic-volatility jump-diffusion models, as well as in high-frequency data analysis and the asymptotic theory of power variations, have spurred a renewed interest among researchers in the area of jump modelling. Numerous studies analysing the jump dynamics emerged in the recent years, identifying effects such as jump self-exciting and clustering effects (Fulop et al, 2014;Fičura and Witzany, 2016), contagion effects between the jumps in different markets (Ait-Sahalia et al, 2015;Fičura, 2015), as well as positive impact of jumps on the future asset price volatility (Corsi et al, 2010;Bandi and Reno, 2016).…”

Section: Introductionmentioning

confidence: 99%

Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies

Fičura¹

2019

Prague Economic Papers

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Profitability of a trading system based on the momentum-like effects of asset price jumps was tested on four currency markets (EUR/USD, GBP/USD, USD/CHF and USD/JPY) and three futures markets (Light Crude Oil, E-Mini S&P 500 and VIX), on 7 frequencies (1-minute to 1-day), over a period of more than 20 years. The proposed trading system entered long and short trades in the direction of asset price jumps and held the positions for a fixed horizon, optimized on the insample period. The system achieved statistically significant out-sample profits for the USD/CHF, EUR/USD and GBP/USD exchange rates, especially on the 15-minute, 30-minute and 1-hour frequencies, with expected returns of up to 20-30% p.a., including transaction costs. On the 1-day frequency, on the USD/JPY and on the three analysed futures markets, only insignificant profits or losses were achieved. On the 1-minute frequency, the system ended with a loss for all of the assets.

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“…As for generalizations of the SVVG jump models, we point the reader to self‐excited jump models of Fulop et al for particle methods and sequential Bayesian learning. Soyer asks about multivariate models generated by gamma subordination of Brownian motion.…”

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confidence: 99%

Rejoinder to “Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns” Reply to the discussions by Nalini Ravishanker and Refik Soyer

Warty

Lopes

Polson

2018

Appl Stoch Models Bus & Ind

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Many thanks to Professors Nalini Ravishanker and Refik Soyer for their insightful comments on our paper. There are clearly many interesting extensions yet to be done for particle learning (PL) with stochastic volatility and jumps. For example, Ravishanker asks about high-frequency trading and how to adapt the stochastic volatility with variance-gamma (SVVG) family and both Ravishanker and Soyer ask about multivariate extensions so that the methods can be applied to a large universe of stocks. These are clearly challenging problems. Soyer also asks about applications and extensions to sequential decision problems such as portfolio allocation.Our main insight is that PL methods are tailor-made to address these problems. This is due to the use of an augmented state vector that needs to be tracked in real time, making the storage requirements of PL minimal. Smoothing would require extra computational effort, but generally, prediction is the goal of asset-pricing studies and again PL is directly suited to these problems and constructs the predictive distribution by averaging over particles and thus takes account of full uncertainty. For particle filtering in the high-frequency context, we refer the reader to the works of Lopes and Tsay 1 and Stroud and Johannes. 2 As for generalizations of the SVVG jump models, we point the reader to self-excited jump models of Fulop et al 3 for particle methods and sequential Bayesian learning. Soyer asks about multivariate models generated by gamma subordination of Brownian motion. This seems totally feasible as particle methods can be naturally constructed when the process is generated by subordination; see, for example, the work of Li. 4 As for multivariate models, we refer the reader to the MCMC work of Lopes et al, 5 which could be implemented via PL by taking advantage of the inherent parallelization of their recursive dynamic regressions. Finally, Ravishanker asks about prior sensitivity and computational efficiency. Such issues arise in particle filters as in MCMC and have to be addressed carefully. The parameter that has the largest issue is as it does not admit a conditional sufficient statistic. Soyer asks about sequential decision problems where again augmented simulation methods are attractive 6,7 and where particle methods have a bright future as an alternative to reinforcement machine learning methods.

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Self-Exciting Jumps, Learning, and Asset Pricing Implications

Cited by 87 publications

References 37 publications

Volatility jumps and macroeconomic news announcements

Volatility jumps and macroeconomic news announcements

Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies

Rejoinder to “Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns” Reply to the discussions by Nalini Ravishanker and Refik Soyer

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