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

Article Source Here: Illiquidity Premium in the Bitcoin Options Market

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

Article Source Here: Net Gamma Exposure in International Markets

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

Originally Published Here: Incorporating Memory and Stochastic Volatility into Geometric Brownian Motion Model

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

Originally Published Here: How Will Bitcoin ETF Options Impact The Markets?

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.

Originally Published Here: Reexamining the Performance of Passive Options Strategies

An asset is a resource owned or controlled by a company that can yield future economic benefits. If these benefits span several years, the asset may be considered a capital asset.

What is a Capital Asset?

A capital asset is a significant property or equipment owned by a business and utilized in its operations to generate revenue. These assets are typically long-term investments with a useful life extending beyond one year, including buildings, machinery, vehicles, and land. Unlike current assets, which are expected to be converted into cash or consumed within a year, capital assets are intended for ongoing use, contributing to the company's operational capacity over time.

Capital assets also depreciate or amortize over their useful life, accounted for as a non-cash expense in financial statements. When acquiring them, businesses evaluate the return on investment (ROI) due to the substantial financial commitment involved. In accounting, capital assets are recorded on the balance sheet at their historical cost, reflecting the purchase price and any additional expenses necessary to make the asset usable.

What are the types of Capital Assets?

Capital assets can be classified into several types based on their nature and purpose within a business. The two most common distinctions include tangible and intangible capital assets. However, other classifications may also exist.

Tangible assets

Tangible capital assets refer to physical items a business uses in its operations and can touch or see. This category includes land, buildings, machinery, and equipment.

Intangible assets

Intangible capital assets are non-physical assets that provide value to a business through rights and privileges. This category includes patents, trademarks, copyrights, and goodwill.

Natural resources

Natural resources are capital assets that consist of raw materials and resources extracted from the environment. This category includes mineral rights, which grant ownership over minerals such as oil, gas, or metals beneath the land, allowing extraction and sale.

Investment assets

Investment capital assets refer to assets held primarily for generating income or appreciation rather than for direct business operations. It includes real estate investments, stocks, and other financial derivatives.

Capital Asset vs Fixed Asset: What are the differences?

Most people often confuse capital and fixed assets. However, they are different based on the following points.

Definition

Capital assets are significant properties or equipment businesses own and use to generate revenue, including tangible assets like land and buildings and intangible assets such as patents and trademarks. Contrastingly, fixed assets are a specific type of capital asset, focusing solely on tangible long-term items like machinery and vehicles used to produce goods and services, not intended for sale.

Scope

The scope of capital assets is broader, encompassing tangible and intangible assets, investments, and natural resources that can yield future economic benefits. In comparison, fixed assets are limited to tangible items used in operations over the long term, excluding intangible assets and financial investments.

Depreciation

Many capital assets, especially tangible ones, are subject to depreciation, while intangible assets like patents are amortized. On the other hand, fixed assets consistently undergo depreciation, allocating their cost over their useful life to account for wear and tear.

Accounting treatment

Capital assets are recorded on the balance sheet at historical cost and can fall under tangible or intangible, with varying accounting treatments based on asset type. In contrast, fixed assets focus on depreciation and impairment accounting.

Conclusion

A capital asset is a resource owned or controlled by a company that provides long-term benefits. Typically, these assets may fall into the tangible and intangible categories. However, there might be other classifications of capital assets as well. While they have similar characteristics as fixed assets, capital assets cover a broader range of resources.

Post Source Here: Capital Asset: Definition, Model, Types, Examples, Accounting Treatment, vs Fixed Asset

Delta hedging is a risk management strategy used to neutralize the impact of price movements in the underlying asset of an option. It involves adjusting the position in the underlying asset to offset the sensitivity of the option's value, measured by its "delta."  Delta represents the rate of change in an option's price relative to changes in the price of the underlying asset. As the price of the underlying asset fluctuates, the delta also changes, requiring frequent rebalancing of the hedge.

Equity index option traders often use delta to hedge vega risks. This approach is feasible due to the strong negative correlation between the equity index and its implied volatility. Reference [1] formalized this practice by developing a so-called mean-variance (MV) delta. Essentially, the mean-variance delta is the Black-Scholes delta with an additional adjustment term. The authors pointed out,

This paper has investigated empirically the difference between the practitioner Black-Scholes delta and the minimum variance delta. The negative relation between price and volatility for equities means that the minimum variance delta is always less than the practitioner Black-Scholes delta. Traders should under-hedge equity call options and over-hedge equity put options relative to the practitioner Black-Scholes delta.

The main contribution of this paper is to show that a good estimate of the minimum variance delta can be obtained from the practitioner Black-Scholes delta and an empirical estimate of the historical relationship between implied volatilities and asset prices. We show that the expected movement in implied volatility for an option on a stock index can be approximated as a quadratic function in the option’s practitioner Black-Scholes delta divided by the square root of time. This leads to a formula for converting the practitioner Black-Scholes delta to the minimum variance delta. When the formula is tested out of sample, we obtain good results for both European and American call options on stock indices. For options on the S&P 500 we find that our model gives better results that either a stochastic volatility model or a model based on the slope of the smile.

In summary, the new delta hedging scheme using the MV delta performs well for certain underlyings, even outperforming stochastic volatility models like SABR and local volatility models.

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

References

[1] John Hull and Alan White, Optimal Delta Hedging for Options, Journal of Banking and Finance, Vol. 82, Sept 2017: 180-190

Originally Published Here: Hedging Vega Risks with Delta

Companies must record an accurate value for inventory in their financial statements to represent an accurate financial picture. However, this task is not straightforward due to the challenges in evaluating inventory. Therefore, it is crucial to understand what inventory valuation is and how it works.

What is Inventory Valuation?

Inventory valuation involves determining the monetary value of a company's inventory at a specific time. This valuation is crucial for financial reporting since it affects the cost of goods sold (COGS) and gross and net income. Accurate inventory valuation ensures that a company's financial statements reflect its accurate financial position and performance, providing stakeholders with essential information for decision-making.

There are several techniques for valuing inventory, each with distinct implications for financial reporting. Some common methods include First-In, First-Out (FIFO), Last-In, First-Out (LIFO), and Weighted Average Cost. Each method impacts the calculation of COGS and the valuation of ending inventory differently, influencing profitability and tax obligations.

What are the methods of Inventory Valuation?

Companies must use one of the various inventory valuation methods. The usage may differ based on policies, accounting standards, and jurisdictional limitations. Usually, the most commonly used inventory methods include the following.

First-In, First-Out (FIFO)

The FIFO method assumes that the oldest inventory items get sold first. It means that the costs associated with the oldest inventory are used to calculate the cost of goods sold (COGS). The remaining inventory gets valued at the cost of more recently acquired items. During rising prices, FIFO typically results in lower COGS and higher net income, as older, cheaper costs are matched against current revenues.

Last-In, First-Out (LIFO)

The LIFO method operates on the premise that the most recently acquired inventory items are sold first. As a result, the COGS reflects the cost of the latest inventory purchased, while older inventory remains on the balance sheet. During inflationary periods, LIFO can lead to higher COGS and lower taxable income since the more expensive, recent costs are deducted from revenues.

Weighted Average Cost

This method calculates the average cost of all inventory items available for sale during the period and applies this average cost to both COGS and ending inventory. The weighted average cost is determined by dividing the total cost of goods available for sale by the total number of units available to sell. This approach smooths out price fluctuations and provides a consistent valuation method.

What is the importance of Inventory Valuation?

Inventory valuation is essential for several reasons, particularly its impact on financial reporting and tax implications. Accurate inventory valuation directly influences the cost of goods sold (COGS), which affects gross profit and net income on financial statements. This accuracy enables stakeholders, including investors and creditors, to assess the company's financial health effectively.

Additionally, the chosen valuation method can significantly impact tax liabilities; for example, using the Last-In, First-Out (LIFO) method during inflation can lead to lower taxable income and improved cash flow, making strategic inventory valuation crucial for financial planning. Moreover, effective inventory valuation is critical for efficient inventory management and performance measurement.

Conclusion

Inventory valuation is a technique that helps derive inventory value at a specific time. While it may sound straightforward, companies face several challenges during the process. Companies use one of the three prominent methods to evaluate inventory, including FIFO, LIFO, and weight average cost. Each of these methods has its advantages and disadvantages.

Post Source Here: Inventory Valuation: Definition, Methods, Meaning, Adjustment, Importance

Catastrophe bonds, or CAT bonds, are a type of risk-linked security designed to transfer the financial risk of natural disasters from insurers to investors. These bonds are typically issued by insurance or reinsurance companies to cover significant losses caused by events such as hurricanes, earthquakes, or floods. Investors in CAT bonds receive higher yields compared to traditional bonds, but they risk losing part or all of their principal if a specified catastrophe occurs during the bond's term.

Pricing models for catastrophic risk-linked securities have primarily followed two methodologies: the theory of equilibrium pricing and the no-arbitrage valuation framework. Reference [1] proposed a pricing approach based on the no-arbitrage framework, offering valuable insights into how CAT bonds are priced.

In their model, the authors utilize the CIR stochastic process model for interest rates and the jump-diffusion stochastic process model for losses. They pointed out,

In this paper, we first discussed the notions of CAT bonds, then described how these bonds were modeled using mathematical concepts of finance and then derived a PIDE and a first-order differential equation through the semi-discretization approach. In this equation, one of the components connected with these securities, the market price of risk of damage, is unavailable; a quadratic term was built using market ask and bid prices to determine this variable. This quadratic equation was improperly formulated; thus, we used the Tikhonov regularization to transform it into a near-initial problem. Then, using the Euler-Lagrange equation, we obtained a Poisson PDE equation. Finally, we provided an approach and numerical findings for determining the market price of risk.

We find the stochastic model, equation (1), to be particularly insightful and effective in describing catastrophic losses.

This year has witnessed numerous hurricanes across Asia, Europe, and America, leading to significant claims for insurers. This paper represents a contribution to advancing risk-sharing practices in the insurance industry.

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

References

[1] S. Pourmohammad Azizi & Abdolsadeh Neisy, Inverse Problems to Estimate Market Price of Risk in Catastrophe Bonds, Mathematical Methods of Statistics, Vol. 33 No. 3 2024

Originally Published Here: No-arbitrage Model for Pricing CAT Bonds

Borrowing and lending can be confusing - however, it's one of the main forces of the economy.

Without borrowing or lending, many people would not be able to afford big purchases like a house or car, and businesses would struggle to grow and expand.

However, with borrowing and lending comes risk - both for the borrower and lender. This is where financial guarantees come in and understanding it is crucial for any individual or business looking to take on a loan.

What is a Financial Guarantee?

When people or businesses take out a loan, they may be asked to provide a financial guarantee as security for the lender.

A financial guarantee is basically a promise made by a third party (usually a bank or insurance company) to pay off the borrower's debt if they are unable to do so.

This gives the lender assurance that their money will be repaid, even if the borrower defaults on their loan.

It's a very common practice in the financial world, and it can come in different forms such as a letter of credit, deposit account, or even a cash collateral.

By understanding how it works, borrowers can improve their chances of getting loans approved and lenders can mitigate the risk of losing money.

Importance of Financial Guarantee

Financial guarantees play a crucial role in the financial world - here are some of the key reasons.

1. Reduces Risk for Lenders: Lending money always comes with risk. By requiring a financial guarantee, lenders have a safety net in case the borrower is unable to make their loan payments.
2. Increases Borrowing Power: Having a financial guarantee can strengthen an individual or business's creditworthiness. This means they are seen as less risky by lenders, which can increase their chances of getting approved for loans and potentially at more favorable terms.
3. Boosts Confidence in Investments: Financial guarantees are often used in investment transactions such as bonds or stocks, giving investors added protection that their investments will be repaid with interest.
4. Ensures Fulfillment of Obligations: In some cases, a company may be required to provide a financial guarantee to fulfill obligations under a contract. This can help build trust and reassurance in business relationships.
5. Mitigates Risk for Borrowers: While it may seem like financial guarantees only benefit lenders, they can also protect borrowers from having their assets seized or credit damaged if they are unable to make loan payments.

Different Types of Financial Guarantees

There are various types of financial guarantees, each serving a specific purpose. However, we can categorize them into two different groups

1. Personal Financial Guarantees

This is basically when people borrow money for personal reasons, such as buying a home or financing education, and need someone to co-sign the loan.

The cosigner provides a financial guarantee that they will cover the borrower's payments if they are unable to do so. A common example would be when students need a cosigner for their student loans.

2. Corporate Financial Guarantees

Another type of financial guarantee is when a company guarantees the obligations of another company, usually one within the same corporate group.

This can be seen when a parent company provides a financial guarantee for its subsidiary or when one company guarantees the debts of another to secure better loan terms.

It's a common practice in the business world, especially when companies want to expand or take on new projects that require significant financing.

Conclusion

Financial guarantees are very common in both personal and business financing. They provide assurance to lenders, help mitigate risk for borrowers, and come in various forms depending on the specific situation. It's important to understand the different types of financial guarantees and their implications before entering into any agreements involving them. It's advisable to talk to a professional who knows the ins and outs of a financial guarantee.

Post Source Here: Financial Guarantee: Definition, Types, Meaning, Importance, Example