The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Towards a skewness index for the Italian stock market

The present paper is a first attempt of computing a skewness index for the Italian stock market. We compare and contrast different measures of asymmetry of the distribution: an index computed with the CBOE SKEW index formula and two other asymmetry indexes, the SIX indexes, as proposed in Faff and Liu (2014). We analyze the properties of the skewness indexes, by investigating their relationship with model-free implied volatility and the returns on the underlying stock index. Moreover, we assess the profitability of skewness trades and disentangle the contribution of the left and the right part of the risk neutral distribution to the profitability of the latter strategies. The data set consists of FTSE MIB index options data and covers the years 2011-2014, allowing us to address the behavior of skewness measures both in bullish and bearish market periods. We find that the Italian SKEW index presents many advantages with respect to other asymmetry measures: it has a significant contemporaneous relation with both returns, model-free implied volatility and has explanatory power on returns, after controlling for volatility. We find a negative relation between volatility changes and changes in the Italian SKEW index: an increase in model-free implied volatility is associated with a decrease in the Italian SKEW index. Moreover, the SKEW index acts as a measure of market greed, since returns react more negatively to a decrease in the SKEW index (increase in risk neutral skewness) than they react positively to an increase of the latter (decrease in risk neutral skewness). The results of the paper point to the existence of a skewness risk premium in the Italian market. This emerges both from the fact that implied skewness is more negative than physical one in the sample period and from the profitability of skewness trading strategies. In addition, the higher performance of the portfolio composed by only put options indicates that the mispricing of options is mainly focused on the left part of the distribution

Clustering and Classification in Option Pricing

This paper reviews the recent option pricing literature and investigates how clustering and classification can assist option pricing models. Specifically, we consider non-parametric modular neural network (MNN) models to price the S&P-500 European call options. The focus is on decomposing and classifying options data into a number of sub-models across moneyness and maturity ranges that are processed individually. The fuzzy learning vector quantization (FLVQ) algorithm we propose generates decision regions (i.e., option classes) divided by ‘intelligent’ classification boundaries. Such an approach improves generalization properties of the MNN model and thereby increases its pricing accuracy

Extending generalised Leland option pricing models: simulation using Monte Carlo

To explain option pricing movements, most studies modify the Black-Scholes model by adding other factors. The parametric generalisation, on the other hand, frequently leads to an over-parametrisation problem in the model being constructed. The model's high constraints frequently resulted in considerable underpricing of the option. The nonparametric generalisation of the Black-Scholes-Merton (BSM) model, on the other hand, is prone to both discretisation and truncation issues in pricing options. Thus, this study extends the existing option pricing models by developing Extended Generalised Leland (EGL) models based on the implied adjusted volatility introduced in Leland models. The integrated framework ensures a model-free modelling while conforming to the conventional parametric option pricing. The proposed semiparametric models are developed to incorporate the transaction costs rate factor in the intermediated model-free framework to assure realistic pricing of options. The main focus of this study is to document by simulation that the EGL models deliver option pricing outperformance compared to the benchmark model. The simulation of the EGL models is conducted to investigate whether the proposed models are practical to be applied in a real financial system. Superior option pricing accuracy was observed in the EGL models based on the simulation results. This finding is grounded on the RMSE values as well on pairwise percentage difference values