How accurately we can predict expectations of EU (in)efficient financial
报告信息
主讲人 Josip Arnerć,Associate professor,University of Zagreb
点评人章韬副教授 上海对外经贸大学经济系
时 间 2018年11月23日（周五）上午10:00—11:30
主讲人简介
Josip是萨格勒布大学经济系副教授，在欧盟金融市场方面有深入的研究，有多篇相关研究发表在重要的国际期刊上。2010年于斯普利特大学获得数量经济学博士学位，本科毕业于斯普利特大学经济系，硕士毕业于萨格勒布大学数量经济系。主要研究领域：计量模型与方法，金融时间序列与波动，随机过程和风险控制。
内容简介
Extracting information that is embedded in financial asset prices is helpful to both market participants and public authorities, especially in implementing monetary policy, because this information reflects market expectations in the future. In this context, futures and option prices can be used for extracting market expectations due to their forward-looking nature.Due to availability of data and digital technology, a prediction of market expectations became interesting and popular area of research, but also challenging for markets with infrequent option trading and illiquid option contracts. Therefore, options prices can be less informative about market expectations;especially at emerging markets where such data are not available. Moreover,option trading does not exist in some EU countries.
Extracted information is mostly concentrated on implied moments (mean, standard deviation, skewness and kurtosis) computed from a certain option pricing model. For example one can invert Black-Sholes option pricing model to extract implied volatility using observable option prices in the market. Extracting important but unobservable parameters from option prices is not limited to implied volatility, i.e. in asimilar manner as implied volatility is derived from option prices, the entire distribution; so called “implied probability distribution” can also be estimated. Hence, the result is the ex ante probability distribution that market participants would have expected if they were risk neutral. This means that the estimated implied probability distribution does not take into account the degree of risk aversion of investors. The risk-neutral probability distribution can describe different characteristics (moments) of the ex ante probability distribution. Hence, variations over time in the implied moments should provide a good indication of changes in the market’s assessment of future developments in the underlying asset.
Few questions arises here, that should be discussed: a) which option pricing model should be used, b) how we can select horizon to expiration date, c) what is prediction accuracy of the given model, d) which prediction accuracy measures are appropriate, e) which comparison method should be used, f) is there a benchmark of unknown, but true, future distribution that we can use for comparison purpose, g) does (in)efficiency of EU financial markets help us in prediction of markets expectations?