Advanced Realized Volatility and Quarticity

Volatility estimators are especially valuable in modelling financial returns and capturing time-variability of financial series. The ongoing $100k Optiver Realized Volatility Prediction Competition on Kaggle has further sparked interest in the research of high-frequency data and revitalized the interest in the field. In the following articles we introduced time-series data and realized volatility as well as financial … Read more

Simple Return, Log Return and Volatility easy intro

Following $100k Optiver Realized Volatility Prediction Competition on Kaggle, interest in financial time-series and concepts has drastically increased. Besides, in this website we have not travelled past the very introductory concepts in time-series ecnnometrics. Therefore in this blog post we are going to introduce the concepts of simple returns, log returns and realized volatility. In … Read more

Cross-sectional data – An easy introduction

Econometric data sets come in numerous shapes, forms and types such as cross-sectional, time-series and panel data. The data type affects the analysis and estimation methods that we as data scientists can use. In this article we are introducing the concept of cross-sectional data. A cross-sectional data set consists of a sample of units such … Read more

Time series data – An easy introduction

A time series is a collection of observations on at least one variable ordered along single dimension, time. A time series data demonstrates properties such as large data size, abundant attributes and continuity. Time series data is particularly useful in an analysis of a trend and forecasting in macroeconomics. In the field of finance time … Read more

Panel data Econometrics – An easy introduction with Python

Panel data (or longitudinal data) set comprises time-series for each cross-sectional unit in a data set. In other words, in a panel data set we take into account the same cross-sectional units over multiple time points. For example, we can consider units such as countries, cities, firms, households, individuals. In this context, we can think … Read more