Model risk and model choice in the case of barrier options and bonus certificates

Baule, Rainer; Shkel, David

doi:10.1016/j.jbankfin.2021.106307

Cited by 6 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for hedging purposes, a local-volatility model has been found to perform worse in some cases (e.g., Dumas et al (1998); Hagan et al (2002)). Additionally, Baule and Shkel (2021) showed empirically that issuers in the German market for bonus certificates (where down-and-out puts are embedded) prefer models with stochastic volatility, whereas local-volatility is not likely to be used. Since jumps are more important in the case of overnight trading gaps, we additionally used a model with jumps.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…This paper analyzes time-discrete hedging of European down-and-out put options near the barrier in the case of overnight trading gaps. Such barrier options are not only traded overthe-counter, but are also embedded in certain types of retail derivatives, for example, bonus certificates (Baule and Tallau 2011;Baule and Shkel 2021;Hernández et al 2008), bonus certificates plus , flex bonus certificates or (multi) barrier reverse convertibles (Wallmeier and Diethelm 2009). In these markets, banks act as market makers and continuously quote prices for their issued products during trading hours.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time-Discrete Hedging of Down-and-Out Puts with Overnight Trading Gaps

Baule

Rosenthal

2022

JRFM

Self Cite

View full text Add to dashboard Cite

Hedging down-and-out puts (and up-and-out calls), where the maximum payoff is reached just before a barrier is hit that would render the claim worthless afterwards, is challenging. All hedging methods potentially lead to large errors when the underlying is already close to the barrier and the hedge portfolio can only be adjusted in discrete time intervals. In this paper, we analyze this hedging situation, especially the case of overnight trading gaps. We show how a position in a short-term vanilla call option can be used for efficient hedging. Using a mean-variance hedging approach, we calculate optimal hedge ratios for both the underlying and call options as hedge instruments. We derive semi-analytical formulas for optimal hedge ratios in a Black–Scholes setting for continuous trading (as a benchmark) and in the case of trading gaps. For more complex models, we show in a numerical study that the semi-analytical formulas can be used as a sufficient approximation, even when stochastic volatility and jumps are present.

show abstract

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time-Discrete Hedging of Down-and-Out Puts with Overnight Trading Gaps

Baule

Rosenthal

2022

JRFM

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Evolutionary Machine Learning in Finance

O’Neill,

Brabazon

2023

Genetic and Evolutionary Computation

View full text Add to dashboard Cite

Model risk and model choice in the case of barrier options and bonus certificates

Cited by 6 publications

References 45 publications

Time-Discrete Hedging of Down-and-Out Puts with Overnight Trading Gaps

Time-Discrete Hedging of Down-and-Out Puts with Overnight Trading Gaps

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

Evolutionary Machine Learning in Finance

Contact Info

Product

Resources

About