Basic forex strategies - page 5
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This paper provides new insights in the skewness risk premium in the stock market. By building strategies which take position in the individual skewness of the constituents of the SP100, we show that the skewness risk premium becomes positive and significant for almost all the stocks after the 2007-2009 financial crisis. We find that this is due to a drastic increase (in absolute value) in the price of the skewness, while we do not find any significant change in the realised skewness of the returns. Consistently, we find that the shape of the average implied volatility smile across stocks becomes steeper after the crisis. Moreover, we find that this pre/post crisis structural change does not apply to the market skewness risk premium, computed as the skew premium of the index SP500.(i) Are the lexica consistent in their analytic ability to produce stock reaction indicators, including volatility, detrended log trading volume and return?
(ii) To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)?
(iii) Are the news of high attention firms diffusing faster and result in more timely and efficient stock reaction?
(iv) Is there a sector specific reaction from the distilled sentiment measures?
We find there is significant incremental information in the distilled news flow. The three lexica though are not consistent in their analytic ability. Based on confidence bands an asymmetric, attention-specific and sector-specific response of stock reactions is diagnosed.
The first article investigates modeling asymmetric volatility in the context of research methods explored by Hassan. The second research study involves oil future prices and term structures, whereby understanding the permanent and transitory shocks in oil futures can be accomplished via a structural vector auto-regression model by Zha. The third article of inquiry is by Cao and Guo which involved delta hedging performance methodologies. In the fourth research study, Ankirchner and Heyne suggested how to use research methods using cross hedging with stochastic correlations. In the fifth article, Srinvasan investigated stock market volatility and used different volatility models that are GARCH-types. The sixth peer-review study investigated is by Menkhoff, which involves currency momentum and the use of moving averages. The seventh research article was about how to price currency options and the methods used to determine which volatility model performed the best proposed was by Manzur, Hoque, and Poitras. The eighth scholarly study, which was authored by Jiang, involves foreign exchange markets and the use of a vector error correction model.
The ninth intellectual inquiry investigated was on tail risk management and some of the methodologies used when modeling with Value-at-Risk and conditional Value-at-Risk by Kayan, Lee, and Pornrojnangkool. The tenth article explores the hydroelectric power industry and how to incorporate a hedging strategy and test for performance by Fleten, Brathen, and Nissen-Meyer. The eleventh research study investigated was by Frikha and Lemaire involved the gas and electricity spot price using a multi-factor model that can present higher volatility markets. The twelfth scholarly article proposed was by Hinnerich which explores equity swaps and demonstrates how to incorporate a jump diffusion model to capture price dynamics. The thirteenth study relates to derivative pricing using a close-form approximation relying on series expansions by Kristensen and Mele. The fourteenth study in this section involves how to build a trading algorithm system by Moldovan, Moca, and Nitchi. The last article reviewed was by Viebig and Poddig, whereby extreme value theory and copula theory was considered as a way to model multivariate daily return distributions of hedge funds.
In the conclusion section of this Depth component a discussion on the synthesis of the relevant research related to research design used in quantitative finance was conducted. Comments on how to approach the research design with a focus on establishing hedging and pricing strategies of financial assets was shown. The intent of this section was to explore some of the tools developed in statistical analysis that enable researchers in quantitative finance to evaluate different hedging and pricing strategies. With a better research design and the use of advanced statistical methods researchers and practitioners can evaluate their financial modeling performance more accurately.
Within the conclusion section each of the fifteen research articles mentioned above will be summarized in the framework of research methods that can promote social change. In addition to the summary of these research studies, some questions are explored to provide possible investigational paths.
Over fifty percent of the respondents stated they had been trading for more than four years. The survey found that more than half of the traders had experienced account-closing losses with nearly 40% have experienced this at least twice. The most common cause of these losses were the use of trades sizes that were too large, with nearly half of all traders stating this was the cause of their worst trade. Additional ‘worst trade’ factors were identified as allowing losing trades to run for too long and the lack of automated stop loss levels. Less than a quarter of traders identified their ‘system’ as being the cause of either their best or worst trades, with ‘best’ trades being attributed to significant market moves over 40% of the time closely followed by allowing winning trades to run for a long time. Only a third of traders said they regularly checked the bid-ask spread before placing a trade with only a quarter ever checking the interest swap charges, despite nearly half of all traders saying they kept trades open overnight. When asked what single area a trader would like to improve, most traders focused on physiological issues rather than system ones.
The purpose of this paper is to share these results with the Retail Trader community and to seek further input as to the best way to help address some of the identified issues.