Volatility effect

Community Alpha of QuantConnect – Part 2: Social Trading Factor Strategies

13.August 2021

This blog post is the continuation of series about Quantconnect’s Alpha market strategies. Part 1 can be found here. This part is related to the factor strategies notoriously known from the majority of asset classes.

Overall, the factors on alpha strategies provide insightful results that could be utilized. The results particularly point to excluding the most extreme strategies based on various past distribution’s characteristics.

Stay tuned for the 3rd and 4th part of this series, where we will explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.

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Markowitz Model

14.June 2021

We present a short article as an insight into the methodology of the Quantpedia Pro report – this time for the Markowitz Portfolio Optimization. As usually, Quantpedia Pro allows the optimization of model portfolios built from the passive market factors (commodities, equities, fixed income, etc.), systematic trading strategies and uploaded user’s equity curves. The current report helps with the calculation of the efficient frontier portfolios based on the various constraints and during various predefined historical periods. The backtests of the periodically rebalanced Minimum-Variance, Maximum Sharpe Ratio and Tangency portfolios will be available at the beginning of July.
Additionally, there is a Case Study dedicated to this Quantpedia Pro tool.

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Risk Parity Asset Allocation

7.May 2021

This article is a primer into the methodology we use for the Portfolio Risk Parity report, which is a part of our Quantpedia Pro offering. We explain three risk parity methodologies – Naive Risk Parity (inverse volatility weighted), Equal Risk Contribution and Maximum Diversification. Quantpedia Pro allows the design of model risk parity portfolios built not just from the passive market factors (commodities, equities, fixed income, etc.) but also from systematic trading strategies and uploaded user’s equity curves.

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An Analysis of Volatility Clustering of Equity Factor Strategies

8.April 2021

Volatility clustering is a well-known effect in equity markets. In simple meaning, volatility clustering refers to a tendency of large changes in asset prices to follow large changes and small changes in asset prices to follow small changes. This interesting effect can be sometimes uncovered as one of the reasons for the functionality of some selected trading strategies. For example, low-volatility months in stock indexes (like the S&P 500 Index) are usually also months with higher performance. As volatility tends to cluster, a low volatility month in the present can signal a low volatility month with a better performance also in the future.

Based on this, we will be testing two hypotheses: (1) firstly, if there is a volatility clustering anomaly present in equity factor strategies; (2) secondly, if there is any performance pattern related to volatility.

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Probability Distributions of Bull and Bear Market States

22.January 2021

Numerous academic papers have shown that the options markets are not only the place where the supply and demand for options meets. For example, they might point out to the smart money positioning, help to assess risk in the form of implied volatility, or be base of the well-known fear index VIX. Novel research of Bhansali and Holdom (2021), uses information embedded in options markets to construct a probability-weighted mixture of two distributions of bull and bear market states for the S&P 500 index. The results show that the implied return distributions drastically change switching from normal to stressed market states and vice versa. Moreover, the uncertainty in both distributions changes in the same fashion.

An excellent example is the shift of distribution before and after the recent US presidential election, which can be found below. Many have feared that if the democrat candidate Biden wins the elections, it would be a bad signal for the markets. However, after the uncertainty has passed, the fear has seemed to disappear. Additionally, the paper also shows how to use the bimodality in return distributions for the asset allocation using various utility functions. Allocations are made using a risky asset, risk-free and even options. Indeed, this research is worth reading. 

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