Risks of Short-dated Options Order Flow

Options, particularly short-dated ones, are gaining popularity among retail traders, with their trading volume increasing significantly. While some research argues that short-dated options do not impact the market, certain market practitioners hold opposing views.

Reference [1] investigated the risks associated with short-dated options order flow. It examined the effective trading costs of short-dated options, exploring their connection to intraday order flow distribution with the goal of identifying risky patterns that could disrupt a market with high trading volumes. The authors pointed out,

Our analysis documents economically and statistically significant positive relationship between intraday order flow volatility and illiquidity in options market, particularly for ultra-short term options.The effect is pervasive: it holds in the time-series and cross-sectional dimension, and it significantly outweighs the significance of more traditional daily first-moment measures of order flow dynamics, such as volumes or absolute order imbalances. Furthermore, it also outweighs the significance of traditional measures capturing the delta-hedging needs of market makers. These findings suggest that liquidity providers rely primarily on active inventory rebalancing and trade matching throughout the day, with the main source of inventory risk arising from providing liquidity to unbalanced order flows. An exchange-specific analysis further shows that liquidity providers are averse to unpredictable order flows even when they do not directly absorb them, highlighting the role of indirect costs and future liquidity provision risk in the observed relationship.

Our findings underscore the potential risks posed by high volumes in short-term option contracts, which can amplify intraday order flow volatility and challenge market stability. We show that as intraday order flow volatility rises, liquidity providers widen bid-ask spreads to manage the elevated risk, resulting in higher hedging costs for investors increasingly dependent on short-term rollover strategies over long-term hedges. This spread widening, in turn, can impair market efficiency by reducing liquidity and price discovery, which may in turn elevate systemic risk…

In summary, the study finds that,

• Liquidity providers primarily focus on active inventory management and trade matching, using delta-hedging as a secondary risk management strategy.
• Additionally, intraday options order flow has become more volatile since the financial crisis.
• The impact of order flow volatility diminishes with longer maturities, emphasizing the heightened liquidity sensitivity of ultra-short-maturity options.

Ultimately, the study concludes that short-dated options order flow can increase illiquidity, raise trading costs, and destabilize the market.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Pederzoli, Paola and Doshi, Hitesh and Sert, Saim Ayberk, Risky Intraday Order Flow and Equity Option Liquidity (2024). https://ift.tt/KFvhDTZ

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VIX Manipulation: Evidence from SPX Options and Market Data

Market manipulation refers to intentional actions taken to distort the normal functioning of financial markets, often to benefit specific individuals or entities at the expense of others. These actions can include spreading false information, rigging prices, or creating artificial demand or supply. A notable example is the LIBOR manipulation scandal, where several major financial institutions colluded to manipulate the London Interbank Offered Rate (LIBOR), a benchmark interest rate used globally to set borrowing costs for trillions of dollars in loans and derivatives. By submitting false rate estimates, these banks influenced LIBOR to their advantage, impacting everything from mortgages to corporate loans.

Reference [1] examined the alleged manipulation of VIX, the volatility index. Market observers claimed that unidentified manipulators, aiming to influence the index, submitted aggressive orders in out-of-the-money (OTM) S&P 500 index options that are included in the VIX calculation. According to these allegations, manipulators profited from these artificial price movements through positions in VIX-related products such as options and futures.

The paper utilized three datasets obtained from the CBOE, covering the period from January 2011 to August 2018, to conduct the study. The author pointed out,

Based on detailed investor-class level data for the period from January 2011 until August 2018, I examine several relations to identify potential manipulative activity during the VIX settlement period. I show that investors within the customer group (i.e., hedge funds and money managers) move prices during the special opening auction and align their price impact to profit from the resulting settlement deviations. Furthermore, I find that customers spread their trading volume across the options used in the calculation of the settlement value proportional to the respective VIX sensitivity. This is consistent with the optimal trading strategy to manipulate the VIX, as shown by Griffin and Shams (2018). Finally, customers’ exposure adjustments the day before expiration predict price movements during the subsequent special opening auction. This suggests that customers anticipate the direction of the VIX movements, which is consistent with manipulation.

Over the sample period from June 2011 to August 2018, settlement deviations caused market distortions that amount to $4.7 billion. Customers benefit from these deviations and earn $91 million over the respective period. These results stress the vulnerability of the VIX settlement process in its current form.

In summary, the author provided evidence supporting claims of VIX manipulation.

This article contributes to improving market transparency and functionality. Let us know what you think in the comments below or in the discussion forum.

References

[1] Manuel Leininger, Essays on Derivatives Markets, University of Konstanz, 2024, Chapter 1

Article Source Here: VIX Manipulation: Evidence from SPX Options and Market Data

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Are Airline Pilots Good Risk Managers?

Due to technological advancements, air travel has never been safer. However, despite all these new technologies, one constant remains: the human factor. Specifically, the most critical individuals are the pilots. Unless we develop pilotless airplanes, a possibility that won't materialize anytime soon, pilot behaviour remains essential.

Do pilots maintain a strong focus on safety and risk management, do they have good mental health?

Reference [1] examines the risk-taking behaviour of airline pilots. The study was motivated by the Boeing 737-300 aircraft crash during a flight from Jakarta to Singapore, which tragically resulted in the loss of all 104 lives on board. One hypothesis proposed during the investigation suggested that the crash may have been intentionally caused by the captain who had been involved in stock trading activities and had allegedly incurred significant financial losses before the incident.

The author pointed out,

Several factors may predispose airline pilots to problematic financial behaviors. First, the presence of novelty-seeking personality traits among pilots, such as high extraversion scores, particularly in facets like assertiveness, activity, and excitement-seeking, may contribute to increased risk-taking in financial decisions. Second, the relatively high salaries of commercial pilots may provide more opportunities for engaging in high-risk financial activities. Third, aviation industry disruptions, like those recently experienced due to the COVID-19 pandemic and potentially in the future due to climate change, may lead to a peculiar set of circumstances, including increased free time, which may exacerbate risk-taking behaviors…

This case report elucidates the potential for high-risk financial behaviors to manifest as a form of behavioral addiction, particularly within the context of occupational stress and disruption. It underscores the imperative for enhanced awareness, targeted screening protocols, and tailored interventions within occupational health settings, with a specific emphasis on safety-critical professions such as commercial aviation... Additionally, empirical studies examining the prevalence of high-risk financial behaviors and pathological gambling among aviators, as well as their potential ramifications on flight safety, would contribute significantly to the existing body of knowledge in this domain.

In summary, airline pilots may be prone to excessive financial risk-taking behaviour.

While this paper primarily presents a case study and does not provide concrete statistics from a significant sample size, it highlights an important trait of airline pilots. Studies like this will certainly contribute to further improving the safety of the airline industry.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Piercarlo Minoretti, From the Flight Deck to the Trading Desk: Gamblified Investing Behavior in a Commercial Airline Pilot, 2024, Cureus 16(8): e66861. DOI 10.7759/cureus.66861

Originally Published Here: Are Airline Pilots Good Risk Managers?

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Trading Volatility Skew: Can Forecasts Increase Returns?

Volatility skew refers to the observed pattern where implied volatility varies depending on the strike price of an option. Typically, in equity markets, out-of-the-money (OTM) put options exhibit higher implied volatility than at-the-money (ATM) or out-of-the-money call options. This phenomenon reflects market participants’ demand for protection against downside risks, as investors are willing to pay a premium for OTM puts to hedge against potential market declines. Understanding volatility skew is crucial for options pricing, hedging strategies, and identifying potential market inefficiencies.

Like volatility, skew can also be forecasted. Reference [1] examines whether skew forecasting models can enhance returns. The paper applied several forecast models to execute skew trades, including ATM straddles, strips, and straps. The authors pointed out,

This study examines the economic significance of using skewness forecasts in option trading. With Black–Scholes option prices both volatility and skewness trading strategies break even with respect to returns and Sharpe ratios at daily intervals, before including transaction costs. After transaction costs are factored in, all strategies result in significant losses. The skewness-based trades are, in general, less profitable compared to volatility trades. Further at weekly or monthly intervals, there is no clear supportive evidence for skewness trades, both before and after transaction costs…

The results indicate that a generalized option-pricing (Corrado & Su, 1996, 1997) model can generate better trading performance for strip and strap trades. The evidence further implies that a generalized option-pricing model, along with forward-looking IVs and conditional skewness forecasts, can generate a superior performance for skewness trades and help outperform the straddle trades.

In summary, skew forecast models, when combined with implied volatilities, can significantly improve the performance of skewness trades. However, trading costs substantially reduce the profitability.

We note that this article is dated, and since then, the index options market has expanded. Additionally, commissions have decreased, and liquidity has improved. It would be interesting to see the same study conducted on the present-day market.

Let us know what you think in the comments below or in the discussion forum.

References

[1] R Jha, M Kalimipalli, The economic significance of conditional skewness in index option markets, Journal of Futures Markets, 2010, 30, 378

Article Source Here: Trading Volatility Skew: Can Forecasts Increase Returns?

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Leveraged Exchange Traded Funds Revisited: Enhancing Returns or Adding Risk?

Leveraged Exchange-Traded Funds (LETFs) are financial instruments designed to amplify the daily returns of an underlying index or asset, often using derivatives and debt. Typically, LETFs aim to achieve a multiple (e.g., 2x or 3x) of the daily performance of the tracked benchmark, both on the upside and downside. LETFs are commonly used for speculative purposes, hedging, or tactical portfolio adjustments, but they require careful management and a solid understanding of the risks involved.

LETFs have received a lot of criticism. Despite the controversy, they remain popular among institutional investors. Reference [1] revisits the use of LETFs in portfolio allocation. The authors pointed out,

Using both closed-form and numerical solutions, we showed that an investor can exploit the observation that LETFs offer call-like payoffs, and therefore could be a convenient way to add inexpensive leverage to the portfolio while providing extreme downside protection.

Under stylized assumptions including continuous rebalancing and no investment constraints, we derived the closed-form IR-optimal investment strategy for the LETF investor, which provided valuable intuition as to the contrarian nature of the strategy. In more practical settings of quarterly trading, leverage restrictions, no trading in the event of insolvency and the presence of margin costs on borrowing, we employed a neural network-based approach to determine the IR-optimal strategies. Our findings show that unleveraged IR-optimal strategies with a broad stock market LETF not only outperform the benchmark more often than possibly leveraged IR-optimal strategies derived using a VETF, but can achieve partial stochastic dominance over the benchmark and (leveraged or unleveraged) VETF-based strategies in terms of terminal wealth.

In short, by using LETFs with active allocation, investors can outperform both buy-and-hold strategies and those that use non-leveraged ETFs.

An interesting finding of this study is that, through a closed-form solution and numerical simulations, the authors demonstrated that LETFs behave like call options. Based on this, it is intuitive that if LETFs are part of a portfolio, they can enhance risk-adjusted returns.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Pieter van Staden, Peter Forsyth, Yuying Li, Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark, 2024, https://ift.tt/Mbv7UQY

Post Source Here: Leveraged Exchange Traded Funds Revisited: Enhancing Returns or Adding Risk?

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Illiquidity Premium in the Bitcoin Options Market

Sometimes, investors come across trading opportunities that offer outsized returns, but they may not fully understand the risks they are taking on. These risks can include operational risks, counterparty credit risks, or hidden optionality within a financial note.

Reference [1] examines the role of liquidity risks in the returns of bitcoin options. In the bitcoin options market, market makers face significant challenges in hedging inventory risk due to price jump risks and lower liquidity. As a result, they charge a higher risk premium. The authors pointed out,

The Bitcoin options market remains notably illiquid, with significant implications for pricing and expected returns. Our analysis reveals that investors, on average, tend to sell options, though this net sell imbalance has lessened with the growing participation of small retail investors. This illiquid market structure leads to a notable illiquidity premium, where higher illiquidity is associated with increased subsequent delta-hedged returns. Using both panel OLS and IPCA factor models, we find a robust and significantly positive relationship between illiquidity and expected option returns, consistent across various illiquidity proxies and model specifications.

The economic rationale behind these findings suggests that the illiquidity premium compensates market makers for the risks and costs associated with market making. Regression analyses indicate that option relative spreads are influenced by delta-hedging and rebalancing costs, inventory costs, and asymmetric information. Importantly, relative spreads remain a significant determinant of expected returns, particularly for options with negative order imbalances, and delta-hedging costs impact returns across the board, implying the presence of additional contributing factors.

In short, Bitcoin options market makers and active traders earn excess returns, partly driven by the illiquidity premium.

This research is noteworthy as it provides insights into the returns of options strategies in the Bitcoin options market. With Bitcoin ETF options now beginning to trade, liquidity is expected to improve, potentially reducing or even eliminating the illiquidity premium.

Let us know what you think in the comments below or in the discussion forum.

References

[1] C Atanasova, T Miao, I Segarra, TT Sha, F Willeboordse, Illiquidity Premium and Crypto Option Returns, Working paper, 2024

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Net Gamma Exposure in International Markets

Net Gamma Exposure (NGE) and its effect on stock prices has been an active research topic recently. Reference [1] applied this concept to the Chinese stock market, studying the NGE effect on intraday stock direction and the relationship between futures and options.

Specifically, the paper presents evidence supporting the idea that market makers' trading activities are a driving force behind the significant reversal effects observed in China’s futures and options markets. The first test provides direct evidence from the perspective of gamma hedging, the second test examines the effects from the viewpoint of vega hedging, and the third test explores the responsibility of Chinese market makers to provide liquidity. The authors pointed out,

Based on the 1-min high-frequency data of China’s commodity futures and options market from 2017 to 2022, this article examines the intraday momentum effect of China’s commodity futures and options. The research of this article found that China’s options and futures markets have significant intraday reversal effects, and that the overnight and opening factors (ONFH) and intraday factors (ROD) can predict the market’s return in the last half hour (LH). Comparing the overnight opening factor (ONFH) and the intraday factor (ROD), this article finds that most of the time (futures, call options), the intraday factor is a better predictor, but for put options, the predictive ability of the overnight opening factor is more significant…

More importantly, this article provides three novel evidence that links market intraday reversal with market makers’ trading behavior. We first explore the Gamma exposure, and find that negative gamma will lead to a stronger intraday reversal effect. Then, we test the prediction between futures’ volatility with option price, and point out that the Vega Hedge demand is one of the sources of the cross-effect between futures and options. Thirdly, this article tests the liquidity to intraday reversal effect. Chinese market makers tend to close accumulated positions when liquidity is high. We divide the sample into a high liquidity group and a low liquidity group. The regression results show that when the market liquidity is sufficient, China’s commodity futures and commodity options show a significant intraday reversal effect; when the market liquidity is lacking, show a significant intraday trend effect.

In short, similar to the US counterpart, NGE, which is caused by market makers, leads to a strong intraday reversal effect. Interestingly, the demand for vega hedging is also identified as one of the sources of the cross-effect between futures and options.

We note, however, that since market makers' positions are not publicly available, daily options open interest was used as a proxy to estimate the market makers’ NGE.

Let us know what you think in the comments below or in the discussion forum.

References

[1] L. Zheng and X. Luo, Is there an intraday reversal effect in commodity futures and options? Evidence from the Chinese market, Pacific-Basin Finance Journal 88 (2024) 102534

Originally Published Here: Net Gamma Exposure in International Markets

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Incorporating Memory and Stochastic Volatility into Geometric Brownian Motion Model

Geometric Brownian Motion (GBM) is a widely used mathematical model for simulating the random behavior of asset prices in financial markets. It assumes that the price of an asset follows a continuous-time stochastic process, where the logarithmic returns are normally distributed. GBM is foundational in option pricing models like Black-Scholes-Merton.

Despite its widespread use, the GBM model has limitations. Reference [1] addresses these limitations by incorporating long memory (long-range dependence) and stochastic volatility into the GBM framework. Three models were studied,

• Model 1, the classic GBM, which excludes both memory and stochastic volatility,
• Model 2, the fractional geometric Brownian motion (FGBM), which includes memory but ignores stochastic volatility, and
• Model 3 incorporates both memory and stochastic volatility.

The study empirically analyzes these models by forecasting the Euro exchange rate against three currencies: Saudi Riyal (SAR), US Dollar (USD), and Australian Dollar (AUD). The authors pointed out,

Exchange rates play a crucial role in the financial trade of any country, especially in international trade. Therefore, understanding the future direction of exchange rates is a priority for stakeholders. To achieve this goal, many researchers in the literature have proposed several models. In this study, the researchers utilized three GBM-based models to predict the exchange rates of three currency pairs: EUR/USD, EUR/SAR, and EUR/AUD. The first model followed the traditional GBM approach without considering memory or assuming stochastic volatility. The second model, known as GFBM, incorporated memory but ignored the assumption of stochastic volatility. Finally, the third model, also a type of GFBM, took both memory and stochastic volatility into account. After performing predictions with all three models, it was observed that the third model demonstrated superior performance, as evidenced by its lowest Mean Squared Error (MSE). This result indicates that incorporating memory and assuming stochastic volatility in GBM positively impacts its effectiveness as a tool for predicting exchange rate prices. Therefore, given the high accuracy shown by model 3, it can confidently be used for forecasting future exchange rates.

In short, the findings suggest that incorporating long-range memory and stochastic volatility significantly enhances the model's predictive power.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Mahan Farzina, Mehdi Sadeghi Moghaddamb, Amir Mohammad Shahbalaei Kashan, The Effects of Adding Memory and stochastic volatility in the GBM Method for Predicting the Euro Exchange Rate, Applied Innovations in Industrial Management 4-1 (2024) 30–41

Article Source Here: Incorporating Memory and Stochastic Volatility into Geometric Brownian Motion Model

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How Will Bitcoin ETF Options Impact The Markets?

Bitcoin ETF options started trading last week. The debut of Bitcoin ETF options was met with significant bullish sentiment, as over 80% of trades were call options. Investors exhibited strong optimism about Bitcoin's future price, with many purchasing options at a strike price of $100,000. This launch marks an important development in the crypto derivatives market, sparking discussions about its potential implications.

With this introduction of Bitcoin ETF options, a natural question arises: how will they impact the crypto market specifically and the financial market more broadly? Reference [1] presents an essay on the impact of Bitcoin ETF options on the market. Although there is no data to support the argument yet, the author utilizes Gold ETFs and gold options as a case study. The author pointed out,

The launch of spot Bitcoin ETF options marks a pivotal step in integrating cryptocurrencies into the broader financial ecosystem. These instruments provide a regulated avenue for accessing Bitcoin derivatives, potentially increasing market liquidity, attracting greater institutional involvement, and contributing to price discovery. These developments could enhance the credibility and stability of the cryptocurrency market over time, supporting its mainstream adoption.

Despite these benefits, Bitcoin ETF options carry significant risks that demand careful consideration. Market volatility, regulatory uncertainties, and operational complexities present challenges for both individual and institutional investors. Engaging with these instruments requires a thorough understanding of their mechanics, as well as a clear assessment of one's risk tolerance and investment goals. Investors must adopt robust risk management strategies to mitigate potential downsides and optimize their exposure.

In short, the author argues that Bitcoin ETFs might actually stabilize market volatility. Additionally, he highlights several risks associated with the Bitcoin ETF options market, including market, regulatory, and operational risks.

Let us know what you think in the comments below or in the discussion forum.

References

[1] David Krause, Bitcoin ETF Options: Implications for Market Liquidity, Volatility, and Institutional Adoption, Preperint, 2024

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Reexaming the Performance of Passive Options Strategies

More than 40 years ago, Merton et al. published two papers [1,2] examining the performance of passive options strategies. They concluded that these strategies outperformed the traditional buy-and-hold approach. At the time of their studies, options data was not widely available, so they used historical volatility to calculate options prices. Merton et al. conducted their research by simulating the impact of options on two portfolios: a broad market proxy of 136 equities and the Dow Jones 30 index. Using a twelve-year period, the backtest incorporated historical volatility and applied the Black–Scholes-Merton model to price the options.

Since then, the options market has become highly liquid, with significant structural changes. A recent article [3] reexamines the strategies studied by Merton et al., along with additional strategies, using actual options data from the period 2012 to 2023. The strategies studied include Call-Write strategies (with seven variants), Put-Write strategies (with two variants), and the Protective Put (PPUT) strategy.

The authors pointed out,

The recent review shows that the original option strategies recommended by Merton et al. no longer provide a favorable return-to-risk ratio. It is likely that these returns were illusory, driven by their assumptions. After all, they were based on a simulation.

Recent data demonstrate that simple options strategies no longer add value to a portfolio or an index. However, our research shows that three well-known and somewhat dynamic option strategies have outperformed the S&P 500 Index on a return-to-risk basis. Furthermore, we find that the favorable performance observed in previous studies can be revitalized by incorporating simple signals of the market regime in their construction.

An interesting finding of this study is that PPUT consistently outperforms the S&P 500 Index on a return-to-risk basis. Even more astonishing is that by adding the simple logic of avoiding puts after a one-standard-deviation draw down, it outperforms the index on a return basis with significantly lower risk. A key reason for the PPUT index and the logic-based PPUT strategy outperformance may be that as more firms implement covered call strategies, they inadvertently reduce implied volatility levels, underpricing the risk in the tails of the distribution. This hypothesis needs to be tested with a larger set of indices.

In short, none of the simple options strategies have outperformed the S&P 500. Interestingly, the PPUT strategy outperforms the buy-and-hold approach on a risk-adjusted basis, and the VIX is shown to be an effective regime filter.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Merton, Robert C., Myron S. Scholes, and Mathew L. Gladstein. 1978. The Returns and Risk of Alternative Call Option Portfolio Investment Strategies. Journal of Business 51: 183–242.

[2] Merton, Robert C., Myron S. Scholes, and Mathew L. Gladstein. 1982. The Returns and Risks of Alternative Put-Option Portfolio Investment Strategies. Journal of Business 55: 1–55.

[3] Andrew Kumiega, Greg Sterijevski, and Eric Wills, Black–Scholes 50 Years Later: Has the Outperformance of Passive Option Strategies Finally Faded?, International Journal of Financial Studies 12: 114.

Article Source Here: Reexaming the Performance of Passive Options Strategies

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