Discover successful case studies and learn how the NumXL solution helped our customers with their analysis and modeling of real-world problems.
In this study, we examine the daily prices of the first four (4) contracts of WTI CL futures listed on NYMEX, compute the number of days to the delivery month for each contract, and carry out principal component analysis (PCA) in an attempt to uncover the core drivers behind the futures curve changes (i.e. level and general shape).
In this case, we examine the highway retail price ($/Gallon) for “No.2 Ultra Low Sulfur (0-15 ppm) Diesel” in the EIA nine (9) PADD regions and carry on principal component analysis in an attempt to find a minimal subset of the principal components that capture (or explains) the variation (spreads) in prices across different regions with a minimal loss of information.
In this paper, we will apply simple linear regression to calculate the CAPM beta for two technology stocks: Microsoft (MSFT) and IBM, then validate the model’s assumptions, identify influential data-points (e.g. outlier), ensure structural stability over the data sample, and make adjustments.
In this paper, we’ll examine a claim by a portfolio manager (let’s call him trader B) about his ability to generate statistically significant Alpha. Alpha is the excess-return compensation for the risk borne, and thus commonly used to assess active managers’ performances. Using plain summary statistics and empirical distribution plots (e.g. Histogram, QQ-Plot), we identified a single data-point with relatively high-return, and once excluded from the sample, the so-called alpha simply vanished.
The case deals with the predictability of the daily closing EUR/USD exchange rate, given its prior history. First, we constructed a trading strategy where we buy EUR at market open, and close the position (selling EUR) at the market close and calculate the daily return time series. After thorough statistical analysis, we found those daily returns behave much like a white-noise. No predictability power.
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