Hello, Habr! Not only traders but also mathematicians and programmers work with stock markets. Director of Engineering at Luxoft Artem Sosulnikov tells about data, which specialists of quantitative hedge funds work with, things they pay attention to, and conditions in such companies.
In this article, I will tell about my participation in the first (out of two so far) Telegram blockchain contest. I didn't win any prize. However, decided to combine and share the unique experience from the start to finish line, so my observations could help anyone who is interested.
Since I didn't want to write some abstract code, instead make something useful. I created instant lottery smart-contract and website which shows smart-contract data directly from Telegram Open Network (TON) avoiding transitional storage.
The article will be particularly useful for those, who want to write their first smart-contract in TON but has difficulties where to start.
Using the lottery smart-contract as an example, I will show the path from the environment set up till publication of the smart-contract and its interaction. Moreover, I will create a website that will show smart-contract data.
The last decades the world economy regularly falls into this vortex of financial crises that have affected each country. It almost led to the collapse of the existing financial system, due to this fact, experts in mathematical and economic modelling have become to use methods for controlling the losses of the asset and portfolio in the financial world (Lechner, L. A., and Ovaert, T. C. (2010). There is an increasing trend towards mathematical modelling of an economic process to predict the market behaviour and an assessment of its sustainability (ibid). Having without necessary attention to control and assess properly threats, everybody understands that it is able to trigger tremendous cost in the development of the organisation or even go bankrupt.
Value at Risk (VaR) has eventually been a regular approach to catch the risk among institutions in the finance sector and its regulator (Engle, R., and Manganelli S., 2004). The model is originally applied to estimate the loss value in the investment portfolio within a given period of time as well as at a given probability of occurrence. Besides the fact of using VaR in the financial sector, there are a lot of examples of estimation of value at risk in different area such as anticipating the medical staff to develop the healthcare resource management Zinouri, N. (2016). Despite its applied primitiveness in a real experiment, the model consists of drawbacks in evaluation, (ibid).
The goal of the report is a description of the existing VaR model including one of its upgrade versions, namely, Conditional Value at Risk (CVaR). In the next section and section 3, the evaluation algorithm and testing of the model are explained. For a vivid illustration, the expected loss is estimated on the asset of one of the Kazakhstani company trading in the financial stock exchange market in a long time period. The final sections 4 and 5 discuss and demonstrate the findings of the research work.
