Volatility effect

How Retail Loses Money in Option Trading

23.August 2022

Over the last few years, we may have noticed a significant growth in retail investing. No surprise, the COVID pandemic outbreak increased the numbers even more, and undoubtedly, options trading is no exception. According to the authors (de Silva, Smith, Co), retail traders seek options expecting spikes in volatility and, for that reason, incline toward firms with more media coverage. Furthermore, their trading increases around the time of firms’ earnings announcements. As a result, market makers benefit from the behavior mentioned above, which causes a large flow of money from retail to market makers.

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How Often Should We Rebalance Equity Factor Portfolios?

10.May 2022

Quantpedia has already covered a countless number of factor investing strategies and articles, from strategies in our Screener to multiple blog posts. Therefore, we can confidently say that we do like factor investing. However, there is always new research with a unique point of view. For example, we recently found a paper focused on the decay of the factor exposures of equity factor strategies. The study examines five factors: Value, Momentum, Quality, Investment, and Low Volatility, across 12 developed and emerging markets over a 20-year period. This research aims to find out how long it takes for a factor to decay after the portfolio is assembled. In other words, how often should the portfolio be rebalanced? 

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What’s the Relation Between Grid Trading and Delta Hedging?

23.February 2022

Delta hedging is a trading strategy that aims to reduce the directional risk of short option strategy and reach a so-called delta-neutral position. It does so by buying or selling small increments of the underlying asset. Similarly, grid trading is a trading strategy that buys/sells an asset depending on its price moves. When the price falls, it buys and sells when the price rises a certain amount above the buying price. This article examines the similarities between delta hedging and grid trading. Additionally, it analyzes numerous versions of grid trading strategies and compares their advantages and disadvantages.

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Periodicity in Cryptocurrencies – Recurrent Patterns in Volatility and Volume

11.January 2022

The high-frequency data in cryptocurrency markets is available at any time of the day, which facilitates the studies of periodicity measures beyond what’s possible in other markets. The research paper by Hansen, Kim, and Kimbrough (2021) investigates the periodicity in volatility and liquidity in two major cryptocurrencies, Bitcoin and Ether, using data from three exchanges, Binance, Coinbase Pro, and Uniswap V2. In particular, the authors measure relative volatility and relative volume across days, hours, and minutes. Their results have confirmed the presence of recurrent patterns in volatility and volume in studied cryptocurrencies for the periods day-of-the-week, hour-of-the-day, and within the hour.

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Community Alpha of QuantConnect – Part 4: Composite Social Trading Multi-Factor Strategy

18.November 2021

This blog post is the continuation (and finale) of series about Quantconnect’s Alpha market strategies. This part is related to the multi-factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but the investment universe is reduced based on the insights of the previous part. So, without further ado, we continue where we have left last time.

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What Drives Volatility of Bitcoin?

5.November 2021

Extremely high bitcoin returns and drawdowns come hand in hand with significant volatility. As Bitcoin is becoming an unignorable part of finance with substantial institutional participation, it is necessary to understand the key drivers of returns and volatility, which is comparably persistent as in other, more established asset classes. In addition, other cryptocurrencies are extremely correlated with Bitcoin, so understanding of key drivers of Bitcoin volatility might also carry to other cryptos. The research of Lyócsa et al. (2020) examines several possible drivers of the volatility. The authors study the realized volatility and its jump component and identify whether the volatility is influenced by various factors such as news about the regulation of bitcoin, hacking attacks on bitcoin exchanges, investor sentiment, and various types of macroeconomic news. The study identifies the significant impact of two intuitive factors: news about the regulation or cryptocurrency exchange hacks. Lagged volatility is also an essential factor, as shown by regression analysis. Regarding macroeconomic data, economic fundamentals do not seem to influence the volatility, except for forward-looking indicators (e.g., the consumer confidence index). Lastly, the authors study the investor sentiment extracted from Google searches, but only the positive sentiment has some impact. Overall, the research is a vital addition to the literature that helps us understand Bitcoin’s volatility.

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