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The variance risk premium defined as the difference between risk neutral variance and physical variance is one of the most crucial information recovered from option prices. It does not, however, reflect the asymmetry in upside and downside movements of underlying asset returns, and also has limitation in reflecting asymmetric preference of investors over gains and losses. In this sense, this paper decomposes variance risk premium into downside - and upside-variance risk premium, and then derives the skewness risk premium and examines its effectiveness in predicting future underlying asset returns. Using KOSPI200 option prices, we obtained the following results. First, we found out that the estimated skewness risk premium has meaningful forecasting power for future stock returns, while the estimated variance risk premium has little forecasting power. Second, by utilizing our results of skewness risk premium, we developed a profitable investment strategy, which verifies the effectiveness of skewness risk premium in predicting future stock returns. In conclusion, the empirical results of this paper can contribute to the literature in that it helps us understand why variance risk premium, in most global markets except the US market, has not been successful in forecasting future stock returns. In addition, our results showing the profitability of investment strategies based on skewness risk premium can also give important implications to practitioners.

This paper aims to consolidate and review the literature in the field of market-calibrated option pricing analysis. By doing so, the paper brings out the gaps in the extant literature and makes suggestions for future researchers in the field.

The methodology used in this research is inspired by the works of Ferreira et al. (2016), Jabbour (2013), Lage Junior and Godinho Filho (2010), Seuring (2013) and Sharma et al. (2018). A total of 1,500 papers written on the pricing of options globally are collated from the Web of Science ranging across 2010-2018.

Most of the research papers present mathematical proposals to value options; without calibrating it with real market data points. The authors bring out five important gaps in the extant literature.

This is arguably the first study that consolidates the literature in the field of market calibrated option pricing analysis with a view to suggest directions for future researchers.

In this chapter, we define the “inflation forward rates” based on arbitrage arguments and develop a dynamic model for the term structure of inflation forward rates. This new model can serve as a framework for specific no-arbitrage models, including the popular practitioners’ market model and all models based on “foreign currency analogy.” With our rebuilt market model, we can price inflation caplets, floorlets, and swaptions with the Black formula for displaced-diffusion processes, and thus can quote these derivatives using “implied Black's volatilities.” The rebuilt market model also serves as a proper platform for developing models to manage volatility smile risks.

Through this chapter, we hope to correct two major flaws in existing models or with the current practices. First, a consumer price index has no volatility, so models based on the diffusion of the index are essentially wrong. Second, the differentiation of models based on zero-coupon inflation-indexed swaps and models based on year-on-year inflation-indexed swaps is unnecessary, and the use of “convexity adjustment,” a common practice to bridge models that are based on the two kinds of swaps, is redundant.

In April 2012, JPMorgan Chase & Company was struggling with large losses that had the potential to damage the reputation of the company, of its Chairman, Jamie Dimon, and of its Chief Investment Officer, Ina Drew. The losses arose from large credit derivative trades in the London office that Dimon described as “bad strategy … badly executed … poorly monitored”.

The purpose of this paper is to investigate whether the information sharing in an online discussion forum, over an agricultural market characterized by a large number of small-scale farmers, has an impact on the market prices.

All the comments posted by farmers and traders on four storable items (potato, onion, lemon and apple) in an online discussion forum over 2013–2017 are collected. By using text mining techniques and regression analysis, words characterizing the actions and expectations of farmers and traders on the course of the market price are identified. Then, summary indicators pointing to positive and negative views on prices are calculated. Finally, the relation between these indicators and market prices is analyzed.

The results point to economically significant impacts, as one standard deviation increase in the share of net positive comments is associated with 20, 22, 13 and 10 percent increase in the consumer prices of potato, onion, lemon and apple, respectively within three months.

Overall, this study provides an evidence for the link between information sharing of farmers on online domain and their collaboration in the physical domain. Thus, the study implies that the information synthesized from online discussion forums may actually contain valuable information for researchers and policy makers regarding the behavior of agents even on traditional domains such as agriculture.