- Большое спасибо слушателям и организаторам мероприятия в Allderivatives cafe (Буренину Алексею Николаевичу лично) – душевно посидели. Начало первого ночи, середина рабочей недели, народ не расходится – это бесценно.
- Презентация, как обещал.
- Используемые пакеты: cжатие матриц по Ледуа-Вульфу – пакет corpcor, Алмгрен-Крисс – PortfolioAnalytics. Для оптимизации и BL использовал самописные функции и какие-то вещи, которые “форкнул” из Systematic Investor Toolbox – рекомендую.
- Цитируемые патент и статьи: [Michaud, Michaud, 1998], [Ledoit, Wolf, 2003], [Statman, 2004], [Almgren, Chriss, 2005], [Mikaelyan, 2013], [Didenko, Demicheva, 2013], [Vasilyev, 2014 – pp.7-14].
PS. И бонус: мой MOOC по портфельному управлению на Лекториум: https://www.lektorium.tv/mooc2/30202 – пробный запуск летом, первый “боевой” – в сентябре. Записывайтесь!
The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. The approach for computing fractal dimension using sequence of the minimal covers with decreasing scale is used to decompose volatility into two dynamic components: specific and structural. We introduce two separate models for both, based on different principles and capable of catching long uptrends in volatility. To test statistical significance of its abilities we introduce several estimators of conditional and unconditional probabilities of reversals in observed and predicted dynamic components of volatility. Our results could be used for forecasting points of market transition to an unstable state.
In one of my previous posts I show how simple averaging of opinions of analysts about future stock returns could significantly improve their predictions of market behavior. Still, much space for errors left, and almost all of it concentrated around market crashes. The latter is hard to predict, although there are some interesting approaches. Today I’ll use The CBOE Volatility Index (VIX®) to predict analyst error itself.
Paper develops volatility forecasting model of RUR/USD exchange rate. To forecast volatility we decompose it to components, characterizing fractal structure of financial time series. Using regression analysis we confirm quasi-cyclical time structure for one of the fractal parameter. We then discuss capacity of the method to predict volatility, including forecasting market transition to unsteady state.
(Applied Econometrics, vol. 36(4), pages 79-87. R code and dataset is available upon request: alexander.didenko (at) gmail.com or from my researchgate.com profile).
As a late followup to all the press, summarizing results of 2014 Winter Olympics, I decided to apply data envelopment analysis to find most efficient teams on 2014 Winter Olympics. I’ll use Benchmarking package to estimate efficiencies and ggplot2 + ggthemes to visualize it.
While helping one of my students, Oleg Karapaev, in his struggle with a paper on determinants of divergence between “fair” and observed prices in stocks (written as a part of his bachelor research project), I’ve made some observations perhaps worth sharing with blogosphere. I used some R code and ggplot2 + ggthemes by Jeffrey Arnolds to represent findings visually (important: to replicate code in full you should have an access to Bloomberg and Rbbg package to download data).
Authors develop DEA model for assessing investment performance and calculate quarterly efficiency scores for 46 fund managers from Russian Federation, covering period 2004–2012. Subsequent analysis shows that higher scores come through low net asset value, high level of risk premiums and low level of expected losses. Key impacts to efficiency change are the level of diversification, relative market value of assets, and the age of management company.
(Published in Russian in “Economic Analysis: Theory and Practice” 40 (391), p.48-57)