Model risk adjusted hedge ratios

Alexander, Carol; Kaeck, Andreas; Nogueira, Leonardo M.

doi:10.1002/fut.20406

Cited by 24 publications

(12 citation statements)

References 42 publications

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“…These differences may serve to explain why the particular Brigo-Mercurio model tested in earlier work showed poorer hedging performance than either the Black-Scholes or Heston models [23]. This contrasts sharply with the results presented in this paper.…”

Section: Prior Workcontrasting

confidence: 95%

“…A comparison of the hedging performance of the Black-Scholes, Heston, and Brigo-Mercurio models has been carried out [23]. The Brigo-Mercurio formula [24] is similar to the asymptotic approximation of the exact MT option price: both price a vanilla call option using a risk-neutral distribution that consists of a mixture of normals.…”

Section: Prior Workmentioning

confidence: 99%

“…The Brigo-Mercurio model allows for an arbitrary number of mixture components, while the MT model allows for only two. When we restrict the Brigo-Mercurio model to two mixture components, the option price is a function of four model parameters [23] rather than three for the MT model [1].…”

Section: Prior Workmentioning

confidence: 99%

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Large-Scale Empirical Tests of the Markov Tree Model

Bhat

Kumar²

2015

IJFS

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The Markov Tree model is a discrete-time option pricing model that accounts for short-term memory of the underlying asset. In this work, we compare the empirical performance of the Markov Tree model against that of the Black-Scholes model and Heston's stochastic volatility model. Leveraging a total of five years of individual equity and index option data, and using three new methods for fitting the Markov Tree model, we find that the Markov Tree model makes smaller out-of-sample hedging errors than competing models. This comparison includes versions of Markov Tree and Black-Scholes models in which volatilities are strike-and maturity-dependent. Visualizing the errors over time, we find that the Markov Tree model yields more accurate and less risky single instrument hedges than Heston's stochastic volatility model. A statistical resampling method indicates that the Markov Tree model's superior hedging performance is due to its robustness with respect to noise in option data.

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Section: Prior Workcontrasting

confidence: 95%

Section: Prior Workmentioning

confidence: 99%

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Large-Scale Empirical Tests of the Markov Tree Model

Bhat

Kumar²

2015

IJFS

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“…A correct implementation of the model should separate out an estimation period and use one (constant) set of parameters throughout the out‐of‐sample hedging period . Alternatively, if the calibrated parameters are systematically related to the underlying price process, adjustments of hedge ratios are necessary (see Alexander et al, …”

Section: Introductionmentioning

confidence: 99%

Does model fit matter for hedging? Evidence from FTSE 100 options

Alexander¹,

Kaeck

2011

Journal of Futures Markets

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This study implements a variety of different calibration methods applied to the Heston model and examines their effect on the performance of standard and minimum‐variance hedging of vanilla options on the FTSE 100 index. Simple adjustments to the Black–Scholes–Merton model are used as a benchmark. Our empirical findings apply to delta, delta‐gamma, or delta‐vega hedging and they are robust to varying the option maturities and moneyness, and to different market regimes. On the methodological side, an efficient technique for simultaneous calibration to option price and implied volatility index data is introduced. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark 32:609–638, 2012

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“…Kim and Kim () find that the Heston model outperforms other stochastic volatility models for Kospi 200 options, confirming that jumps offer no significant improvement. Alexander et al () explain how hedge ratios should be adjusted to account for the model risk stemming from time‐varying calibrated parameters in a model that assumes the parameters are constant. The motivation for these investigations is that traders will use the same model for hedging vanilla options as they do for pricing complex options.…”

Section: Introductionmentioning

confidence: 99%

Regime‐dependent smile‐adjusted delta hedging

Alexander¹,

Rubinov²,

Kalepky³

et al. 2011

Journal of Futures Markets

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We introduce several regime-dependent smile-adjusted deltas and compare their efficiency with the smile-adjusted deltas that are popular with option traders. Using years of daily option prices, out-of-sample hedging performance tests for options of all moneyness and maturities and daily, weekly, or fortnightly rebalancing show that even the simplest regime-dependent smile-adjustment consistently outperforms implied BSM delta hedging and local volatility and minimum variance smile-adjustments. Markov-switching deltas offer the best performance, with delta-hedging errors often half the size of implied BSM hedging errors. During volatile markets risk reduction from regime-dependent delta hedging is much greater than during tranquil periods

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Model risk adjusted hedge ratios

Cited by 24 publications

References 42 publications

Large-Scale Empirical Tests of the Markov Tree Model

Large-Scale Empirical Tests of the Markov Tree Model

Does model fit matter for hedging? Evidence from FTSE 100 options

Regime‐dependent smile‐adjusted delta hedging

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