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See detailData Filtering Rules in Option Valuation
Bams, Dennis; Blanchard, Gildas; Lehnert, Thorsten UL

E-print/Working paper (2018)

The choice of data filtering rules are, next to model selection and parameter calibration, an important step in an option pricing exercise. We illustrate the implications of data filtering rules, by ... [more ▼]

The choice of data filtering rules are, next to model selection and parameter calibration, an important step in an option pricing exercise. We illustrate the implications of data filtering rules, by investigating three alternative renowned filtering rules in the context of pricing European S&P 500 index options. Different filtering rules result in strongly diverging samples, which carry different information and therefore lead to different parameter estimates. This is illustrated for the Ad Hoc Black-Scholes model. No filtering rule is, in terms of pricing performance, superior on the whole range of options. Instead, each filtering rule is specialized toward better pricing of options types that were included in the calibration sample at the costs of excluded options. Included options are unable to perfectly represent the properties and characteristics of excluded options. In particular, option prices are heterogeneous in the maturity dimension, which is a major driving force underlying the impact of exclusion filters on pricing performance. Additionally, small deviations from the put-call parity strongly affect parameter estimates as well as the accompanying pricing performance. These results emphasize the prominent role of filtering rules as an important implicit choice for an option pricing model calibration. [less ▲]

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See detailMarket Skewness Risk, Risk Aversion and the Cross-Section of Stock Returns
Bams, Dennis; Honarvar, Iman; Lehnert, Thorsten UL

E-print/Working paper (2018)

Previous research suggests that the cross section of stock returns has substantial exposure to risks captured by higher moments of market returns, implied by S&P500 index option prices. However, assuming ... [more ▼]

Previous research suggests that the cross section of stock returns has substantial exposure to risks captured by higher moments of market returns, implied by S&P500 index option prices. However, assuming that risk aversion is time-varying, a risk-based explanation would suggest that the exposure is priced in periods of high risk aversion, while it is not necessarily priced/weaker in periods of low risk aversion. We find that for market skewness and kurtosis, this hypothesis is not supported by the data. We find that each of the higher moment prices of risk is time-varying and has significantly different patterns under different market conditions, proxied by a measure of investors’ relative risk aversion. In particular, in line with our reasoning, our results suggest that only in down-markets (high risk aversion periods), the exposure to the market volatility innovations is priced significantly negative in the cross-section of stocks, while it is not priced in up-markets (low risk aversion periods). In contrast, we find that in down-markets, market skewness and kurtosis are not priced risk factors, while the price of market skewness risk is significantly negative and the price of kurtosis risk is positive in up-markets. However, the previously reported results for skewness and kurtosis are counterintuitive, strictly violate the risk compensation principles and, therefore, do not support a risk-based explanation. The results persist even after controlling for the Fama-French and Carhart factors. [less ▲]

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See detailModel Uncertainty and Pricing Performance in Option Valuation
Bams, Dennis; Blanchard, Gildas; Lehnert, Thorsten UL

E-print/Working paper (2018)

The objective of this paper is to evaluate option pricing model performance at the cross sectional level. For this purpose, we propose a statistical framework, in which we in particular account for the ... [more ▼]

The objective of this paper is to evaluate option pricing model performance at the cross sectional level. For this purpose, we propose a statistical framework, in which we in particular account for the uncertainty associated with the reported pricing performance. Instead of a single figure, we determine an entire probability distribution function for the loss function that is used to measure option pricing model performance. This methodology enables us to visualize the effect of parameter uncertainty on the reported pricing performance. Using a data driven approach, we confirm previous evidence that standard volatility models with clustering and leverage effects are sufficient for the option pricing purpose. In addition, we demonstrate that there is short-term persistence but long-term heterogeneity in cross-sectional option pricing information. This finding has two important implications. First, it justifies the practitioner’s routine to refrain from time series approaches, and instead estimate option pricing models on a cross-section by cross-section basis. Second, the long term heterogeneity in option prices pinpoints the importance of measuring, comparing and testing option pricing model for each cross-section separately. To our knowledge no statistical testing framework has been applied to a single cross-section of option prices before. We propose a methodology that addresses this need. The proposed framework can be applied to a broad set of models and data. In the empirical part of the paper, we show by means of example, an application that uses a discrete time volatility model on S&P 500 European options. [less ▲]

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See detailAre Capital Requirements on Small Business Loans Flawed?
Wolff, Christian UL; Bams, Dennis; Magdalena, Pisa

E-print/Working paper (2018)

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See detailTrade Credit and firm Comovements
Pisa, Magdalena UL; Bos, Jaap; Bams, Dennis

E-print/Working paper (2015)

This paper provides evidence that production linkages, as well as credit chains (represented by trade credit), are important for the transmission of idiosyncratic (firm-level) shocks across firms in the ... [more ▼]

This paper provides evidence that production linkages, as well as credit chains (represented by trade credit), are important for the transmission of idiosyncratic (firm-level) shocks across firms in the economy. We build on the idea that trade credit develops along production linkages, and amplifies the idiosyncratic shock as firms may lack inputs and also liquidity. Using disaggregated firm-level data we show that the disturbance of customer's sales increases with greater trade credit linkage. We show that during a recession the existence of trade credit linkage propagates shocks upstream, from a supplier onto its upstream customer. In these periods, firms are short of liquidity and are unable to withstand a drop in trade credit provision. In good times, however, trade credit plays a stabilizing role, reducing the volatility of firms' sales. In these periods, firms with sufficient liquidity are able to transfer some of it to liquidity-starved production partners in order to guarantee their own stable production. [less ▲]

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See detailThe Impact of Uncertainty in the Oil and Gold Market on the Cross-Section
Lehnert, Thorsten UL; Bams, Dennis; Blanchard, Gildas et al

E-print/Working paper (2015)

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See detailFrom Time Varying Risk-Aversion and Sentiment to Anomalies in Market
Lehnert, Thorsten UL; Honarvar, Iman; Bams, Dennis

E-print/Working paper (2015)

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See detailRipple Effects from Industry Defaults
Bams, Dennis; Pisa, Magdalena UL; Wolff, Christian UL

E-print/Working paper (2014)

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See detailEvaluating Option Pricing Model Performance Using Model Uncertainty
Lehnert, Thorsten UL; Blanchard, Gildas; Bams, Dennis

E-print/Working paper (2014)

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See detailEstimation Risk in Option Pricing
Lehnert, Thorsten UL; Bams, Dennis; Blanchard, Gildas

E-print/Working paper (2013)

Detailed reference viewed: 37 (3 UL)
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See detailModeling default correlation in a US retail loan portfolio
Pisa, Magdalena UL; Wolff, Christian UL; Bams, Dennis

E-print/Working paper (2012)

Detailed reference viewed: 38 (1 UL)
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See detailLoss Functions in Option Valuation: A Framework for Selection
Lehnert, Thorsten UL; Wolff, Christian UL; Bams, Dennis

E-print/Working paper (2008)

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