I have met a surprising number of people who have tried to build a trading algorithm at some point. Here is why you should not waste your time with that. First of all, I have to admit: I do it, too. For a long time stock markets have been endlessly fascinating to me, and I … Continue reading Why you should not try to predict stock markets
Stock market crisis: is there a tennis ball effect?
Stock markets are plummeting as the Corona virus has turned into a global pandemic. During the short periods of recovery one can hear those stock market reporters on TV talking about a tennis ball effect. The idea is that what goes down has to come up again and vice versa. So should you wait for … Continue reading Stock market crisis: is there a tennis ball effect?
Something is wrong with realized volatility
If you own stocks of a company, how big is the risk to lose at least 5 percent of your money tomorrow? To answer that question, you need to know the variance of that stock. The problem with stock market variances is that they change a lot over time. They are very high in times … Continue reading Something is wrong with realized volatility
Visualizing MonteCarlo Simulation Results: Mean vs Median
Simulation studies are used in a wide range of areas from risk management, to epidemiology, and of course in statistics. The MonteCarlo package provides tools to automatize the design of these kind of simulation studies in R. The user only has to specify the random experiment he or she wants to conduct and to specify … Continue reading Visualizing MonteCarlo Simulation Results: Mean vs Median
Introducing the MonteCarlo Package
My first R package has been released on CRAN recently. It is named MonteCarlo and aims to make simulation studies as easy as possible - including parallelization and the generation of tables. What Are Simulation Studies Good For? Monte Carlo simulations are an essential tool in statistics and related disciplines. They are routinely used to … Continue reading Introducing the MonteCarlo Package
The Case Against Seasonal Unit Roots
There are several ways to model seasonality in a time series. Traditionally, trend-cycle decomposition such as the Holt-Winters procedure has been very popular. Also, until today applied researchers often try to account for seasonality by using seasonal dummy variables. But of course, in a stochastic process it seems unreasonable to assume that seasonal effects are … Continue reading The Case Against Seasonal Unit Roots
The Curious Behavior of diffseries()
This is the story of a subtle error that, to my opinion, is a nice example of the special challenges of statistical programming. One of my main research interests is time series with long memory. These are often modeled by fractionally integrated models, where $latex (1-L)^d X_t=\varepsilon_t.$ Here $latex X_t$ is the time series, $latex … Continue reading The Curious Behavior of diffseries()