Contradictions between principles-based and rules-based risk management?

Business analytics, financial analysis, organisational management systems, decision support systems, risk management systems, etc. have been improved in recent years through the implementation of new ICT, Internet and Industry 4.0 information technologies. However, to what extent does the application of new ICT, Internet and Industry 4.0 information technologies in the aforementioned systems of performed analysis, management, etc. generate efficiency gains in the operation of these processes? How and to what extent are risk management systems improved through the application of new ICT, Internet and Industry 4.0 information technologies? Which of the applied ICT, Internet and Industry 4.0 information technologies have contributed the most in terms of increasing the effectiveness of the implementation of risk management processes?

What do you think about this subject?

Please reply,

I invite you all to discuss,

Thank you very much,

Regards,

Dariusz Prokopowicz

Some commercial and investment banks are already creating their cryptocurrencies. Some investment funds are also allocating part of their funds for the purchase of certain investment financial instruments and other investment assets as part of building their investment prortfolio to the purchase of selected cryptocurrencies. It may therefore be that in the future, financial or para-financial institutions will be established that will accept selected cryptocurrencies for deposits and, on the other hand, will also lend cryptocurrencies. Related to this issue will be an increase in the level of institutionalisation of cryptocurrency trading platform markets. As part of a potential increase in the level of formalisation of procedures and institutionalisation of markets in which citizens and businesses use cryptocurrencies, supervisory institutions should also be created, thanks to which the level of security of the use of cryptocurrencies should increase and also the level of investment risk of investing in cryptocurrencies should decrease significantly. Thus, with a higher level of security in the use of cryptocurrencies, more citizens will be willing to use cryptocurrencies both as payment instruments and as alternative investment instruments. With certain financial institutions or para-financial firms lending cryptocurrencies in a formula similar to bank lending transactions, the importance of the credit risk management process of cryptocurrency lending will increase. Since new information technologies and Industry 4.0, including artificial intelligence, machine learning, deep learning and analytics conducted on computerised Big Data Analytics platforms, are already being used in the credit risk management process occurring in the context of banks' lending and investment transactions in securities and other capital markets assets, so in the future such new technologies may also be applied to improve the credit risk management processes of cryptocurrency lending.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

On Monday 13.03.2023, the situation shaping the capital markets was influenced by the developing news in many media that one of the largest banks in the USA, i.e. Silicon Valley Bank, had declared bankruptcy. The collapsed SVB bank was taken over by the state-owned Federal Deposit Insurance Corporation (FDIC, Federal Deposit Guarantee Corporation) on Friday 10.03.2023 after the bank was unable to pay out money to customers withdrawing their deposits in a panic. SVB is the 16th largest bank in the US and has served a significant proportion of Silicon Valley startups, companies and funds. Silicon Valley Bank was the largest collapse in the banking sector since the 2008 Lehman Brothers collapse and the onset of the 2007-2009 global financial crisis. At the end of 2022, SVB had more than $209 billion in assets. But the collapse of Silicon Valley Bank (SVB) is not the end of the problems in the financial markets. On Monday 13.03.2023, news hit the media that another bank is failing. Customers worried about their deposits also called many of their other banks to check that their money was safe. This second spectacular failure in the financial system is New York's Signature Bank, which was shut down by state market regulators on Sunday. According to the Reuters news agency, this is the third largest bank failure in US history. It is also another spectacular bankruptcy of a major financial institution overseas in just a few days. New York-based Signature Bank is a US financial institution that, at the end of 2022, had customer deposits worth almost USD 89 billion and USD 110.36 billion in assets. According to published official figures, almost a quarter of these funds came from cryptocurrencies. This raises a key issue to be resolved regarding the extent to which credit procedures and the credit risk management process at financial institutions have improved over the last 15 years, i.e. after the onset of the 2007-2009 global financial crisis. I have described the determinants and root factors of the 2007-2009 global financial crisis, including the mistakes made in credit risk management, in my articles on this issue, which I posted on my profile of this Research Gate portal after publication. I would like to invite those conducting research on this issue to join me in research collaboration on issues and factors for improving the credit risk management process in financial institutions.

In view of the above, I would like to address the following questions to the esteemed community of scholars and researchers:

What do you think about this topic?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Warm regards,

Dariusz Prokopowicz

I am using the moments-based approach to calculate the risk premium of growing a specific crop. As proposed by Chavas (Kim and Chavas,2003 and Chavas et al,2009) I used CRRA risk preference. Then incorporate moments (variance and skewness) into the equation used by Antle (1987). I got a problem as variance and skewness are in large numbers (into the power of 10^7 sometimes) the calculated risk premium is a huge number. I guessed that there is something wrong here. Can anyone suggest of a source of information of the detailed risk calculation using the moments-based approach?

Are there long-term risk management systems being developed, long-term projects, predicted over the next several decades, adverse climatic changes related to the global warming process?

If so, which institutions do these types of risk management systems for predicted climate change?

Are there scientific research in this area?

Are there published scientific studies that would confirm the need to develop long-term risk management systems, long-term, predictable in the next several decades, unfavorable climate changes related to the global warming process?

Please reply

I invite you to the discussion

Thank you very much

Best wishes

There are 10 different item in the system that needed to be repaired. Each item has its repairing price, and each item has its impact to the system (rather I say here, consequences) if one or more of them were not repaired. But the problem is, I have not enough money to repair all of these items. My money probably just enough for repairing a few items only. And I need to get the system keep running, no matter what. No waiting time.

So, my method to assess these items consequences are with Event Tree Analysis, convert them in number (or level), so I can input them into my Linear Programming to decide which item(s) I am going to repair, and which item(s) I am not.

- Do multiple Event Tree Analysis to the system, with that item(s) not available, to get any Consequences if I decide to not repair that item(s), and its Likelihood. In order to get each items Consequences Number (or Level).

- Do Linear Programming, with repair price and Consequences Number (or Level) of each item, to decide which is going to be repaired, and which is not.

My question is:

1. In your opinion, is this method valid as a selection method?

2. If yes, can I get these Consequence Number (or Level) just by doing Event Tree Analysis? Or is there any method (maybe specific/simplied one) to get these number?

3. Is there any journal or thesis, with the same problem and same method I had?