Reducing Transaction Costs in Volatility-Managed Portfolios

Volatility targeting is a risk and portfolio management technique that adjusts exposure based on changes in asset volatility. We have discussed volatility targeting methods in previous posts, for example, Applying Volatility Management Across Industries. While effective, this technique requires frequent rebalancing. As with any approach involving high turnover, such as delta hedging, transaction costs can significantly erode performance.

Reference [1] investigates the volatility targeting technique under the constraint of transaction costs. Specifically, it introduces a rebalancing boundary, which triggers adjustments only when certain conditions are met. In other words, the target volatility portfolio E, with the rebalancing boundary, modifies its asset allocation only when the ratio of target volatility to asset volatility deviates from the desired ratio by at least a predefined threshold.

The authors pointed out,

To reduce the transaction costs that a target volatility strategy may encounter over an investment horizon, we incorporate a novel rebalancing boundary setup into the volatility target asset allocation mechanism. We identify the optimal rebalancing boundary level that maximizes a portfolio return measure while controlling the portfolio’s risk measure within a constrained optimization setting, focusing on the Omega ratio and mean volatility deviation as the portfolio return and risk measures, respectively. We evaluate the performance of the target volatility portfolio with the optimal rebalancing boundary (“Optimized Portfolio E”) and contrast it with a traditional target volatility portfolio without such rebalancing boundaries (“Portfolio S”) by conducting comparative analysis under different market scenarios. We find that the optimized Portfolio E contains significantly lower transaction costs while generating higher returns than the benchmark Portfolio S. Moreover, the optimized Portfolio E realizes lower portfolio downside risk, as indicated by a higher level of Value-at-Risk (VAR) than Portfolio S. This suggests that our proposed enhancement to the target volatility strategy by adding the rebalancing boundary level does not compromise portfolio risk control.

In short, using a rebalancing boundary reduces transaction costs and improves the portfolio’s risk-adjusted returns.

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

References

[1] Zefeng Bai, Dessislava Pachamanova, Victoria Steblovskaya, Kai Wallbaum, Target volatility strategies: optimal rebalancing boundary for transaction cost minimization, Financial Markets and Portfolio Management, 2025

Article Source Here: Reducing Transaction Costs in Volatility-Managed Portfolios

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Jumps and Volatility Clustering in AI-Driven Markets

AI-assisted trading is a growing area in quantitative finance. However, concerns have emerged that it may destabilize markets. We recently discussed how trading strategies generated by large language models could introduce new systemic risks to financial markets.

Continuing this line of research, Reference [1] examines how AI trading affects market volatility, liquidity, and systemic risk. The authors used daily data from the S&P 500 index, applying an OLS regression and a Poisson model to estimate the frequency of extreme price jumps, and a GARCH(1,1) model to analyze volatility clustering. They pointed out,

One of the key takeaways is the lack of a strong direct relationship between AI trading and market fluctuations. While AI trading does not appear to significantly drive volatility under normal conditions, its effects may depend on broader market structures, liquidity availability, and macroeconomic shocks. This suggests that AI-based trading systems do not inherently destabilize markets but may interact with other variables in ways that influence financial stability. In periods of normal trading activity, AI may enhance price efficiency and liquidity provision. However, during financial distress or economic uncertainty, algorithmic decision-making could amplify volatility through feedback loops and self-reinforcing mechanisms. The persistence of volatility observed in the GARCH model supports this argument, indicating that once volatility spikes occur, they tend to last longer.

A significant finding of this study is the strong correlation between energy consumption and market volatility. Unlike AI Presence, energy consumption emerges as a key driver of financial fluctuations. The high computational demands of AI trading suggest that energy-intensive models may contribute to extended periods of heightened volatility, raising concerns about both financial stability and sustainability.

In short, the results show that AI-driven trading is positively associated with more frequent market jumps and higher volatility. Interestingly, AI does not impact markets directly, but rather through the energy consumption used to train the models.

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

References

[1] Zorina ALLIATA, Andreea-Mădălina BOZAGIU, The Impact of AI on Market Volatility: A Multi-Method Analysis Using OLS, Poisson, and GARCH Models, Proceedings of the 19th International Conference on Business Excellence 2025

Article Source Here: Jumps and Volatility Clustering in AI-Driven Markets

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Analyzing Crypto Market Sentiment with Natural Language Processing

Sentiment analysis is a growing research area in quantitative finance, especially with the advancement of Large Language Models (LLMs) and Natural Language Processing (NLP). Sentiment-based trading plays an important role in traditional finance; however, it is even more relevant in DeFi and crypto markets, which are known for their volatility and are significantly influenced by investor sentiment.

Reference [1] utilizes NLP to analyze sentiment in the crypto market and its impact on cryptocurrencies. Specifically, it investigates the relationship between sentiment in financial news articles and cryptocurrency price movements using natural language processing and statistical correlation analysis. The study consists of three phases: sentiment extraction, sentiment clustering, and correlation analysis.

The authors pointed out,

The study finds that Ethereum (ETH) has the strongest correlation between sentiment and price trends, with the hybrid sentiment correlation increasing from 0.3819 to 0.3900 over 24 hours…One possible explanation is Ethereum’s deep integration with DeFi applications and smart contracts, where sentiment-driven narratives (such as network upgrades, ecosystem developments, and new partnerships) significantly impact investor decisions.

Bitcoin exhibits a moderate sentiment correlation (0.2899 hybrid), which slightly increases after 12-24 hours. This suggests that while Bitcoin is affected by sentiment, other macroeconomic factors, institutional trading behaviors, and regulatory developments have a greater influence on its price.

Unlike Ethereum, Bitcoin has established itself as “digital gold” and a macroeconomic hedge, attracting a higher proportion of institutional investors, hedge funds, and long-term holders who base their decisions on fundamental and technical factors rather than short-term sentiment shifts. This makes BTC’s price less reactive to immediate sentiment fluctuations compared to ETH.

XRP demonstrates the weakest correlation with sentiment (0.1005 hybrid, increasing slightly to 0.1205 after 24 hours), suggesting that its price movements are largely disconnected from sentiment-based trading. A major reason for this weak correlation is XRP’s centralized development structure and reliance on partnerships with financial institutions.

In short, the article concluded that ETH shows the strongest sentiment-price correlation, BTC is moderately influenced, and XRP is the least impacted. Traders react to sentiment with a 12–24 hour lag, creating opportunities for predictive trading strategies.

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

References

[1] Franco Farrugia, Cedric Deguara, Sentiment Analysis and Cryptocurrency Price Correlation: A Data-Driven Study, MCAST Journal of Applied Research & Practice 2025; 9 (2) : 166-184

Post Source Here: Analyzing Crypto Market Sentiment with Natural Language Processing

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Decomposing the Variance Risk Premium: Up and Down VRP

The variance risk premium (VRP) is a well-researched topic in quantitative finance. The VRP is the difference between the market-implied variance and the expected realized variance of an asset. Usually, the VRP is positive, reflecting the compensation investors demand for bearing volatility risks. Reference [1] extends this analysis further and breaks down the VRP into two components: an up (UVRP) and a down (DVRP) component.

The authors pointed out,

…we dissect the VRP in terms of upside (UVRP) and downside (DVRP) variance risk premia. The DVRP is the main component of the VRP, and the most important to assess, since investors tend to hedge against downward movements to avoid losing money. Conversely, investors often gravitate toward upside movements and are willing to pay to get exposure to it and the potential for higher profits.

…Thus, the DVRP should be positive (reflecting the compensation required by an agent to bear the downside risk), whereas the UVRP should be negative (viewed as the discount given by an agent to secure a positive return on an investment).

An interesting byproduct of this decomposition is the skewness risk premium, or SRP (simply defined as SRP = UVRP-DVRP), which will be negatively valued by construction. Kozhan, Neuberger, and Schneider (2014) show that compensation for variance and skewness risks are tightly linked.

In addition, this work explores the link between the DVRP and the equity risk premium, or ERP. Current asset pricing research considers that, over shorter time horizons, the VRP provides superior forecasts for the ERP; these periods are less than a year, typically one quarter, ahead (Bollerslev, Tauchen, and Zhou, 2009). To further this exploration, the study also considers the link between the SRP and the ERP at various prediction steps.

In short, the article concluded that,

• DVRP is typically positive, reflecting the premium required to bear downside risk, while the UVRP is typically negative, reflecting a discount for access to upside potential.
• The difference between UVRP and DVRP defines the skewness risk premium (SRP), which is inherently negative and reflects investor preference asymmetry.
• DVRP correlates with the equity risk premium (ERP), especially over short-term horizons such as quarterly forecasts.

This article provides further insights into volatility dynamics. Let us know what you think in the comments below or in the discussion forum.

References

[1] B Feunou, MR Jahan-Parvar, C Okou, Downside variance risk premium, Journal of Financial Econometrics 16 (3), 341-383

Post Source Here: Decomposing the Variance Risk Premium: Up and Down VRP

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Can AI Trade? Modeling Investors with Large Language Models

Large language models (LLMs) are advanced artificial intelligence systems trained on vast amounts of text data to understand and generate human-like language. LLMs can perform a wide range of language tasks, including translation, summarization, question answering, and code generation. Their versatility has made them valuable tools across industries, from finance and healthcare to education and software development.

Reference [1] utilized LLMs to construct trading agents in the financial markets. Specifically, the author used LLMs to emulate various types of investors: value investors, momentum traders, market makers, retail traders, etc. The article pointed out,

First, LLMs can effectively execute trading strategies. They consistently understand market mechanics, process market information, form price expectations, and execute trades according to specific instructions. Their trading behavior is highly sensitive to the prompts they receive—they faithfully follow directions regardless of profit implications…

Second, LLMs react meaningfully to market dynamics. They consider current and historical prices, dividends, and other market information when making decisions. …

Third, market dynamics with LLM agents can resemble actual markets and mirror classic results from the theoretical finance literature. When these agents interact, they produce realistic price discovery and liquidity provision with emergent behaviors, including price convergence toward fundamental values…

These findings carry significant implications for market structure and regulation. While LLM agents can enhance price discovery and liquidity, their adherence to programmed strategies, even potentially flawed ones derived from prompts, could amplify market volatility or introduce novel systemic risks, as observed in our simulated bubble scenarios. A key concern is the potential for widespread correlated behavior: similar underlying LLM architectures responding uniformly to comparable prompts or market signals could inadvertently create destabilizing trading patterns without explicit coordination. This underscores the critical need for rigorous testing and validation of LLM-based trading systems prior to live deployment.

In short, the article concluded that trading strategies generated by large language models are effective, but could introduce new systemic risks to financial markets because these agents would act in a correlated manner.

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

References

[1] Alejandro Lopez-Lira, Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations, arXiv:2504.10789

Article Source Here: Can AI Trade? Modeling Investors with Large Language Models

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Improving Hedging with Skew-Adjusted Delta

Delta hedging is a method used to reduce or eliminate the directional risk of an options position. In most delta hedging schemes, delta is calculated using the Black-Scholes-Merton (BSM) model. However, the BSM delta is not always accurate due to the assumptions embedded in the model. For a more accurate hedge, adjustments need to be made. We have discussed such an adjustment proposed by Hull and White.

Following this line of research, Reference [1] examined the use of skew-adjusted delta for hedging. The paper retested the method developed by Vähämaa [2] and applied it to S&P 500 index options during the COVID-19 pandemic. Specifically, the author modified the BSM delta as follows,

The author pointed out,

Considering the second hypothesis (H2), the performance of the smile-adjusted delta was retested with data from the S&P 500 index. Both models’ quantitative fit is documented to be approximately 90%, but SAD explains daily option price movements slightly better than the BS delta, regardless of the moneyness and maturity of the option. Indicating, it is reliable to finally compare the delta hedging performance of the two deltas.

The overall conclusion is that SAD outperforms the BS delta in delta hedging, meaning that the second hypothesis (H2) is also true. To contradict Vähämaa’s (2004) results, the empirical finding from delta hedging indicates that SAD outperforms the BS delta most distinctly among ITM options. When the hedging horizon is longer than a day, SAD outperforms the BS delta regardless of the moneyness and maturity of the option. That out-performance becomes even more notable as the hedging horizon lengthens. This out-performance increase is even more distinct than what Vähämaa (2004) documented. This may be due to the relatively steep volatility smile making the smile-adjustment term, or specifically considering the slope of the smile, more crucial.

In short, the article shows that skew-adjusted delta (SAD) performs better than BSM delta, especially for in-the-money options. When the hedge lasts more than one day, SAD outperforms BSM delta across all moneyness and maturities, with the advantage growing as the hedging period gets longer.

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

References

[1] Berg, Wille, From the inaccurate Black-Scholes model to more efficient delta hedging with smile-adjusted extension,  University of VAASA, 2025

[2] Vähämaa, S., Delta hedging with the smile, Financial Markets and Portfolio Management, 18(3), 241-255, 2004

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Bitcoin Trend Following Strategies vs. Traditional Indices: A Comparative Study

Trend following is an investment strategy that seeks to capture gains by identifying and trading in the direction of established market trends. Trend followers aim to ride sustained movements, upward or downward, using rules-based systems often driven by price momentum, moving averages, or breakout signals.

Trend following is a popular investment strategy among both individual and institutional investors. Reference [1] examined and compared the performance of the trend following strategy in the U.S., China, and cryptocurrency markets.

The paper applies trend-following strategies using simple, exponential, and double exponential moving averages. For BTCUSD, the S&P 500, and the CSI 300 Index, short windows of 1–20 days and long windows of 21–50 days are used. A buy signal is triggered when the short-term average exceeds the long-term average, and a sell signal when it falls below.

The authors pointed out,

The results of this paper show that: first, the Bitcoin trend-following strategy performs more significantly than the traditional financial index under special market conditions (such as during the COVID-19 epidemic); second, the DEMA trend-following strategy of Bitcoin is relatively stable in terms of Sharpe ratio, especially in the bear market; Third, there is a trend of negative or no correlation between Bitcoin and traditional financial indexes, indicating that Bitcoin may serve as an effective hedging tool.

In short, the study finds that Bitcoin trend-following strategies outperform traditional indices during market stress, show stable Sharpe ratios in bear markets, and exhibit low or negative correlation with traditional assets, suggesting Bitcoin's potential as a hedge.

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

References

[1] L Mo, L Wang, Z Zhang, K Huang, Effectiveness Test of Trend Following Strategy for Emerging Assets and Traditional Assets: A Case Study of Digital Currency and Index Futures, Modern Management based on Big Data VI, 2025

Post Source Here: Bitcoin Trend Following Strategies vs. Traditional Indices: A Comparative Study

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Reducing Path Dependency in Options PnL

The profit and loss of an options trading strategy can be path-dependent, meaning that interim price movements, not just the final outcome, significantly influence profits and losses due to factors like dynamic hedging, early exercise risk, and volatility shifts.

A well-known example is the PnL of a delta-hedged option position, where the outcome depends on the path. Even if we estimate the realized volatility correctly, we still cannot determine the exact profit or loss, because the PnL is path-dependent.

How can we reduce this path dependency?

Reference [1] proposed the use of a laddering technique. The authors pointed out,

Laddering is a practical and effective solution to mitigate the risks of path dependency in option income strategies. Unlike single-path strategies that rely on fixed expirations and trade dates, laddering involves staggering option expirations and strike levels across different time frames. This approach reduces reliance on specific market outcomes, creating a smoother income stream and a more resilient portfolio structure.

Exhibit 28.2 highlights the significant benefits of laddering by combining one-month period for each trading day of the month. The results demonstrate that laddered strategies reduce return variability compared to single-path strategies, which depend heavily on specific market conditions at expiration. By diversifying expirations and strikes, laddering ensures a steady flow of income while minimizing the impact of adverse market events. The laddering approach may also improve liquidity, enabling portfolio managers to incrementally adjust their portfolios more effectively in response to market changes. This makes laddering an indispensable tool for managing path dependency and achieving consistent portfolio performance.

In short, the author advocates the commonly used approach of diversification, specifically, diversifying entry times and strike selections.

This approach is reasonable and widely accepted. However, we note that what the author refers to here is more accurately a mitigation of the negative outcomes resulting from “unlucky” paths. In a covered call strategy, since no dynamic hedging is performed, the final PnL depends on the terminal distribution of the underlying only. Nevertheless, the same technique applies equally to strategies that involve dynamic hedging.

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

References

[1] John Burrello, Managing Path Dependency and Balancing Yield in Option Income Strategies, In: Fabozzi, F.J., de Jong, M. (eds) Derivatives Applications in Asset Management. Palgrave Macmillan, Cham.

Originally Published Here: Reducing Path Dependency in Options PnL

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Harvesting the Equity Risk Premia Through Options

The equity risk premium refers to the excess return that investing in the stock market provides over a risk-free rate, typically represented by government bonds. It compensates investors for taking on the higher risk associated with equities. Estimating the equity risk premium is essential for asset allocation, valuation models, and long-term return expectations in portfolio management.

Reference [1] investigates the use of stock options to capitalize on the equity risk premium. It studied all the U.S. optionable stocks. The study first utilized a machine learning method to estimate expected stock returns (ESR). Then, each month, it sorted at-the-money call options by the ESR of the underlying stock and constructed a long-short portfolio: buying calls on high-ESR stocks and selling calls on low-ESR stocks, holding these positions to maturity. The authors pointed out,

We study via a simple test whether options are a useful tool to harvest the risk premia of the underlying stocks. We introduce a trading strategy that buys calls on stocks with high expected stock returns, and sells calls on stocks with low expected stock returns, and vice versa for puts. We find that these two trading strategies deliver surprisingly low returns, which do not even outperform a naive investment that simply buys all available call or put options, i.e., the “market”…

This finding has two important implications. First, it shows that options are not a useful tool to extract stock risk premia. Second, it implies that option prices are not independent of the underlying’s expected return—violating a central insight of option pricing theory. To corroborate our findings, we apply machine learning techniques to predict expected option returns and option prices. We find that variables predicting stock returns well, do barely predict option returns, but explain option prices well. Moreover, if we use our direct estimate of the expected stock return as a predictor variable, we again find that it predicts price levels well, but not returns.

Finally, we find violations of put-call-parity consistent with our result. In particular, the level of expected stock return is a strong predictor of the implied volatility spread between a pair of calls and puts. This suggest that options are priced such that they largely offset the effects of the underlying’s expected return on the expected option returns.

In short, the article concluded that,

• Options are not effective instruments for capturing stock risk premia,
• Option prices are influenced by the expected returns of the underlying stocks, which challenges a core assumption of traditional option pricing theory.

These findings are interesting and somewhat surprising. However, we note that they apply only to cross-sectional returns. As observed by the authors, if one has a directional bias, then simply buying calls can deliver respectable risk-adjusted returns. Hence, in the time-series momentum space, having a directional edge could be augmented by using options.

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

References

[1] d'Avernas, Adrien and Schlag, Christian and Sichert, Tobias and Sichert, Tobias and Waibel, Martin and Wang, Chunjie, Betting on Stocks with Options?, Swedish House of Finance Research Paper No. 2025-03 https://ift.tt/Ap1GTJQ

Originally Published Here: Harvesting the Equity Risk Premia Through Options

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Seasonality in Return Skewness: the Day-of-Week Effect

Seasonality is a well-studied phenomenon in financial markets. It manifests in returns, volatility, and the volatility risk premium. Despite being well documented, little (if any) research has been carried out on the seasonality of skew.

Reference [1] fills this gap by studying the day-of-week effect in return skewness. It analyzes the five leading stock indices in the US markets from 1928 to 2023. The authors pointed out,

The main contribution of our paper is introducing the day-of-the-week effect into time-varying skewness specifications for stock market return models. In the current study, we find that skewness on Mondays is negative and lower than on Fridays. This suggests that an investor might anticipate frequent small gains and occasional significant losses on Mondays compared to on Fridays. In line with the existing literature, we also report that daily returns are typically negative on Mondays, and expected Monday returns fall below Friday returns across the five leading US stock market indices, indicating the pervasive and persistent nature of the weekend effect. Moreover, Monday returns exhibit higher volatility than Friday returns do. These results are robust across various specifications and subsamples.

Thus, the strategies adopted for investment on days with negative skewness may need to be tailored to manage the inherent risk of substantial losses despite the allure of frequent small gains. Hedging strategies, particularly through options, are vital for downside protection. Emphasising low or no-leverage positions is crucial in such scenarios to mitigate the potential for amplified losses…

In short, Mondays exhibit the lowest and most negative skewness. This research also confirms other research findings, notably,

• Daily returns are typically negative on Mondays, and
• Monday returns show higher volatility than other days.

An interesting follow-up would be to examine the seasonality of the skew risk premium.

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

References

[1] N. Hande Sevgi, Mehmet Baha Karan &M. Hakan Berument, Day-of-the-week effect on stock market returns, volatility, and skewness,  1-24, 2025

Originally Published Here: Seasonality in Return Skewness: the Day-of-Week Effect

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