volatility effect - QuantPedia

Markowitz Model

June 14, 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.

Risk Parity Asset Allocation

May 7, 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.

An Analysis of Volatility Clustering of Equity Factor Strategies

April 8, 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.

An Introduction to Volatility Targeting

March 9, 2021

One of the most popular reports in the Portfolio Analysis section of our Quantpedia Pro tool is “Volatility Targeting”. In this article, we will explain some theory behind this portfolio management method. And then, we will go more in-depth, pick several examples and explain some common volatility targeting variants.

Probability Distributions of Bull and Bear Market States

January 22, 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.

Novel Market Structure Insights From Intraday Data

November 19, 2020

In recent years, financial markets have experienced a boom in passive and index-based strategies, which could have caused a change in the trading volume, volatility, beta or correlations. The reason is straightforward: the index investing causes a lot of stocks to move in the same direction. A novel research Shen and Shi (2020), using high-frequency data, suggests that over the last two decades, the patterns mentioned above have changed and the index investing is the cause. Both the trading volume and stock correlations are increased at the end of trading sessions. Betas are firstly dispersed, but in general, converge to one during the rest of the day. Trading volume has high dispersion at the market open, but low dispersion at the market close. Overall, the paper has many important implications for portfolio managers, risk managers and traders as well since it is closely related to the transaction costs, intraday price fluctuations, correlations or liquidity. Moreover, it is full of exciting charts that are worth seeing.

Implied Volatility Indexes for European Government Bond Markets

October 28, 2020

Volatility indexes are essential parts of the financial markets. They offer investable opportunities and exposure to the volatility, but most importantly, those indexes offer forward-looking measures of option-implied uncertainty. Therefore, such indexes are often used as indicators of risk or sentiment in the markets. For example, the well-known VIX index is often called the fear-index. The volatility indexes are not exclusive to the equity market. There are fixed-income option-implied volatility indexes for US Treasury futures, but the European fixed income market lacks such index. This novel research paper by Jaroslav Baran and Jan Voříšek fills this gap and proposes volatility indexes, connected to the euro bond futures using the Cboe TYVIX (US Treasury implied volatility index) (2018) methodology. As a result, the TYVIX and euro bond futures volatility indexes are directly comparable.

Authors: Jaroslav Baran and Jan Voříšek

Title: Volatility indices and implied uncertainty measures of European government bond futures

The Daily Volatility of Foreign Exchange Rates and The Time of Day

October 15, 2020

The foreign exchange market (FOREX) is opened 24 hours a day, but traders from different parts of the world tend to prefer different trading hours. However, various dominant trading sessions around the globe can lead to time-dependent market characteristics. Novel research by Doman and Doman (2020) has studied how does the daily volatility of FX rates depend on the time of day of calculation. The volatility changes through the day, and the underlying dynamics depend on the time of the estimate. The results can have important implication for practitioners since the volatility differences are large enough so they can influence trading/risk management decisions.

Authors: Małgorzata Doman and Ryszard Doman

Title: How Does the Daily Volatility of Foreign Exchange Rates Depend on the Time of Day at Which the Daily Returns Are Calculated?

The Effectivity of Selected Crisis Hedge Strategies

July 30, 2020

During past months we made a set of articles analyzing the performance of equity factors and selected systematic strategies during coronavirus crisis. These articles were short-ranged with data only from the start of the year 2020, which is enough for the purpose of the quick blog posts, but very short-sighted to see the nature of these strategies. Therefore, we expanded the time range by 20 years. For a better understanding of hedge possibilities of these strategies, we have added a comparison to essential safe-haven assets, not only to equities.

Cryptocurrency Volatility Index

July 23, 2020

Whenever traders want to assess the stock market’s mood, there is one really popular and useful index the most of them turn to. Yes, you guessed it right, it’s CBOE’s VIX Index. And which index can we use if we want to determine the mood of the cryptocurrencies? We can turn to a paper written by Fabian Woebbeking, which offers the methodology to compute two cryptocurrency volatility indexes (CVX & CVX76). The CVX and CVX76 Indexes also extract the market’s expectation of future volatility from option prices, but from options on the Bitcoin. The research suggests that the cryptocurrency option market has finally reached a sufficient market size to extract stable cryptocurrency volatility information.

Authors: Fabian Woebbeking

Title: Cryptocurrency Volatility Markets