Oil Prices and Inflation – The Pakistan data set

Inspired by the work done by two Thai economists, we ran some numbers for crude oil price changes and inflation in Pakistan. Here are some initial results [ad#Grey back med rectangle] Crude Oil and WPI for Fuel, lighting and lubricants 19.29% share of WPI General Crude Oil and CPI for Transport & Communication 7.32% share…Read moreRead more

Oil Prices and Inflation – The Pakistan data set

Inspired by the work done by two Thai economists, we ran some numbers for crude oil price changes and inflation in Pakistan. Here are some initial results Figure 1 – Crude Oil, WPI and CPI changes  

Shades of crude oil – correlations over time – part iii

Here is a quick review of how correlations have behaved over the last 2 years across the many shades of oil. Key benchmark used was Brent and it correlations were tracked against WTI, Fuel Oil, Diesel Oil, Natural Gas, Corn Oil, Soybean Oil and Palm Oil. WTI, Fuel and Diesel were used to provide a…Read moreRead more

The many shades of crude oil – graphical relationships

WTI and Brent Scatter plot – Brent and WTI Form: Linear Strength: Very clear fit of data to a non-horizontal straight line. High concentration of points around the line of best fit indicates strong relationship. Direction: Positive incline from left to right Outliers: No significant deviations. Brent and Fuel Oil Scatter plot – Brent and…Read moreRead more

The many shades of crude oil – part i

Correlation describes the strength and direction of a linear relationship between two quantitative variables. In other words it is a measurement of the degree of association between two sets of numbers that describes how closely they track or are related to one another. Except for Crude Palm Oil the data used for commodities are cash…Read moreRead more

Notation and Terminology updates – what is new

Quick update on pages. Four quick posts introducing some key terms that I am likely to use in my analysis. A reader requested that we shared the assumptions behind some of our oil and currence price volatility graphs. Also added a new glossary page in the header

Value at Risk – Historical Simulation

Historical simulation is a non-parametric approach of estimating VaR, i.e. the returns are not subjected to any functional distribution. VaR is estimated directly from the data without deriving parameters or making assumptions about the entire distribution of the data. This methodology is based on the premise that the pattern of historical returns is indicative of…Read moreRead more

Value at Risk – Methods – Variance Covariance

This method assumes that the daily returns follow a normal distribution. From the distribution of daily returns we estimate the standard deviation (σ). The daily VaR is simply a function of the standard deviation and the desired confidence level. For example, at the 99% confidence level the VaR is equal to 2.33 × σ. To…Read moreRead more

Value at Risk – VaR

VaR is a market risk measurement approach that uses the statistical analysis of historical market trends and volatilities to estimate the likelihood that a given portfolio’s losses will exceed a certain amount. It measures the largest loss likely to be suffered on a portfolio position over a holding period (usually 1 to 10 days) with…Read moreRead more

Volatility trend analysis

Volatility trend analyses were carried out by calculating sixty day moving averages of daily SMA volatilities in the given look-back period. The daily SMA volatility has been calculated based on prior sixty return observations. The graphical depiction of the trend line shows the average volatility of the next sixty volatilities at a given point in…Read moreRead more