Definition of dynamic noise and observational noise in finance

[orochimaru]

Hi,

i have problems differentiating these two terms. can anyone give some examples of these two terms? Thanks!

 

[EnumaElish]

This is the first time that I have encountered these specific concepts. If I had to guess, dynamic noise may be something like an AR (autoregressive) error structure; i.e. e(t) = a₀ + a₁e(t-1) + ... + a_ke(t-k) where e(s) is the noise term in period s. Observational noise may be the empirical error term that a model estimation might produce, e.g. when estimating the CAPM equation r_i(t) = b₀ + b₁r_m(t) + u_i(t) for asset i, observational noise may be $$\widehat {u_i}(t) = r_i(t) - \widehat {b_0} - \widehat {b_1} r_m(t)$$. These are my guesses.

 

[tacman]

Dynamic noise in finance refers to the unpredictable and constantly changing nature of financial markets. It can be caused by various factors such as economic conditions, political events, and investor behavior. This type of noise can make it difficult for investors to accurately predict and make decisions about the market, leading to potential losses.

On the other hand, observational noise in finance refers to the errors or discrepancies that occur in the collection and analysis of financial data. This can be caused by human error, technical issues, or external factors. Observational noise can distort the true patterns and trends in financial data, making it challenging to make accurate predictions and decisions based on this data.

To better understand these terms, let's consider an example. Imagine you are an investor trying to analyze the stock market. The dynamic noise in this scenario would be the constant changes in the market due to various factors such as economic news, company performance, and investor sentiments. This dynamic noise can make it challenging to accurately predict the future direction of the market.

Observational noise, on the other hand, could come into play when collecting and analyzing data on a particular stock. For instance, if there is a technical issue with the data collection process, it could result in incorrect or missing data, leading to observational noise. This can make it challenging to make informed decisions about the stock based on this faulty data.

In summary, dynamic noise and observational noise are both sources of uncertainty and can make it challenging to make accurate predictions and decisions in the world of finance. It is essential for investors to understand and account for these types of noise to minimize potential losses and make informed decisions.