associated with them a particular frequency of observation or collection
of data points. The frequency is simply a measure of the interval over, or
the regularity with which, the data are collected or recorded. Box 1.2 shows
some examples of time series data.
A word on ‘As transactions occur’ is necessary. Much financial data does
not start its life as being regularly spaced. For example, the price of common
stock for a given company might be recorded to have changed whenever
there is a new trade or quotation placed by the financial information
recorder. Such recordings are very unlikely to be evenly distributed over
time -- for example, there may be no activity between, say, 5p.m. when
the market closes and 8.30a.m. the next day when it reopens; there is
also typically less activity around the opening and closing of the market,
and around lunch time. Although there are a number of ways to deal
with this issue, a common and simple approach is simply to select an
appropriate frequency, and use as the observation for that time period
the last prevailing price during the interval.
It is also generally a requirement that all data used in a model be
of the same frequency of observation. So, for example, regressions that seek
to estimate an arbitrage pricing model using monthly observations on
macroeconomic factors must also use monthly observations on stock returns,
even if daily or weekly observations on the latter are available.
The data may be quantitative (e.g. exchange rates, prices, number of
shares outstanding), or qualitative (e.g. the day of the week, a survey of the
financial products purchased by private individuals over a period of time,
a credit rating, etc.).
Problems that could be tackled using time series data:
● How the value of a country’s stock index has varied with that country’s
macroeconomic fundamentals
● How the value of a company’s stock price has varied when it announced
the value of its dividend payment
● The effect on a country’s exchange rate of an increase in its trade deficit.
associated with them a particular frequency of observation or collection
of data points. The frequency is simply a measure of the interval over, or
the regularity with which, the data are collected or recorded. Box 1.2 shows
some examples of time series data.
A word on ‘As transactions occur’ is necessary. Much financial data does
not start its life as being regularly spaced. For example, the price of common
stock for a given company might be recorded to have changed whenever
there is a new trade or quotation placed by the financial information
recorder. Such recordings are very unlikely to be evenly distributed over
time -- for example, there may be no activity between, say, 5p.m. when
the market closes and 8.30a.m. the next day when it reopens; there is
also typically less activity around the opening and closing of the market,
and around lunch time. Although there are a number of ways to deal
with this issue, a common and simple approach is simply to select an
appropriate frequency, and use as the observation for that time period
the last prevailing price during the interval.
It is also generally a requirement that all data used in a model be
of the same frequency of observation. So, for example, regressions that seek
to estimate an arbitrage pricing model using monthly observations on
macroeconomic factors must also use monthly observations on stock returns,
even if daily or weekly observations on the latter are available.
The data may be quantitative (e.g. exchange rates, prices, number of
shares outstanding), or qualitative (e.g. the day of the week, a survey of the
financial products purchased by private individuals over a period of time,
a credit rating, etc.).
Problems that could be tackled using time series data:
● How the value of a country’s stock index has varied with that country’s
macroeconomic fundamentals
● How the value of a company’s stock price has varied when it announced
the value of its dividend payment
● The effect on a country’s exchange rate of an increase in its trade deficit.
การแปล กรุณารอสักครู่..