Volatility and jump risk in option returns

We examine the importance of volatility and jump risk in the time-series prediction of S&P 500 index option returns. The empirical analysis provides a different result between call and put option returns. Both volatility and jump risk are important predictors of put option returns. In contrast, only volatility risk is consistently significant in the prediction of call option returns over the sample period. The empirical results support the theory that there is option risk premium associated with volatility and jump risk, and reflect the asymmetry property of S&P 500 index distribution

Are there gains from using information over the surface of implied volatilities?

We investigate the out-of-sample predictability of implied volatility using the information over the implied volatility surface. We show that implied volatility surface is useful for the out-of-sample forecast of implied volatility up to 1 week ahead. Trading strategies based on the predictability of implied volatility could generate significant risk-adjusted gains after controlling for transaction costs. Significant results also depend on the way of modeling implied volatility surface. We then calibrate a two-factor stochastic volatility option pricing model to implied volatility data. Results show that implied volatility is better explained by both long- and short-term variance factors

The role of credit risk in asset pricing and the price of irrationality

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The role of credit risk in asset pricing and the price of irrationality

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Listen to the Companies: Exploring BIM Job Competency Requirements by Text Mining of Recruitment Information in China

Building information modeling (BIM) is a pivotal technology to realizing the digital transformation of the construction industry. Lack of BIM professionals, however, is one of the reasons the application of BIM technology in the construction industry is hindered. Identifying BIM competency requirements is critical for BIM professionals' training. This paper uses the structural topic model (STM) to mine the topics of BIM recruitment information to deeply understand the BIM competency requirements from a 360° view of the construction industry. The company size, salary level, year of experience, and education in BIM recruitment information are taken as covariates to examine their impact on BIM recruitment topic prevalence. And the changing trend of the topic prevalence and topic correlations are observed through visual analysis. The results reveal that the current BIM competency requirements in the construction industry contain three aspects: management competencies, professional and technical competencies, and personal characteristics. In particular, the requirements for BIM application, construction drawing design, and information technology (IT) skills are relatively strong, and personnel professionalism is also a concern of BIM job recruitment. Companies of different sizes have evident preferences for competencies. Salary levels and years of experience requirements also affect the intensity of corporate demand for BIM competencies. However, education is not the main factor affecting the recruitment of BIM positions. The results can provide a reliable theoretical basis for educational institutions to build a proper BIM professional course system, for companies to develop BIM job recruitment plans, and for individuals to choose their employment goals

Transferrable contextual feature clusters for parking occupancy prediction

Recently, real-time parking availability prediction has attracted much attention since the rapid development of sensor technologies and urbanisation. Most existing works have applied various models to predict long and short-term parking occupancy using historical records. However, historical records are not available for many real-world scenarios, such as new urban areas, where parking lots are fast adjusted and extended. In this paper, we aim to predict parking occupancy using historical data in other areas and contextual information within the targeted area that lacks historical data. We propose a two-step framework to first learn the important contextual features from areas where parking records already existed. Then we transfer these features to the other new areas without historical data records. Through conducting a real-world dataset with various clustering methods combined with different regression models, we observe that multiple contextual features are likely to influence parking availability prediction. We find the best combination (i.e., k-shape clustering algorithm and LSTM regression model) to build parking occupancy prediction model based on the subsequent quantitative correlation analysis between contextual features and parking occupancy. The experimental results show that (1) the conventional internal clustering evaluation does not work well for spatio-temporal data clustering for the prediction purpose; (2) our proposed approach achieves approximately 3% error rate in 30 minutes of prediction, which is significantly better than the estimation of the occupancy rate using the rate in the adjacent regions (13.3%)