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How to calculate PRIIPs KID indicators under ESMA rules?

Sven Peter & Thomas — Wed, 29 Apr 2026 07:41:00 +0000

Estimated read time: 3 minutes

Since the introduction of the PRIIPs framework for Key Information Documents (KIDs), as set out in Commission Delegated Regulation (EU) 2017/653 and further refined in Commission Delegated Regulation (EU) 2021/2268, asset managers are required to publish and periodically update these documents. In practice, regulators observe that KIDs are often missing, outdated or insufficiently maintained.

That is not surprising. The KID is not just a disclosure document, but the outcome of a highly prescribed methodology. Risk, performance and costs all have to be calculated in a specific way. With increasing regulatory scrutiny, it has become essential to apply these rules consistently, update KID indicators on time and ensure correct publication.

The importance of KIDs

KIDs were introduced to improve comparability between investment products within a harmonised European framework. By using a standard format and common calculation methodology, they allow investors to assess different products on a consistent basis.

This standardisation plays a key role in improving transparency, giving investors clearer insight into:

The risk profile of a portfolio
Potential performance under different market scenarios
The impact of costs on investment results

This makes the KID an important bridge between complex portfolio construction and investor-friendly disclosure. Its value depends on whether the figures are calculated correctly and kept up to date.

Key metrics and methodology

The quantitative foundation of the KID is built around a number of core indicators that together provide insight into risk, performance and cost.

At the core of the KID is the Summary Risk Indicator (SRI), shown on a scale from 1 to 7. The SRI is derived from two underlying components: market risk and credit risk.

Market risk is measured through the Market Risk Measure (MRM). For many products, this is based on the Value-at-Risk Equivalent Volatility (VEV), which translates downside risk into a volatility-based metric that can be mapped to a risk class.

Credit risk is captured through the Credit Risk Measure (CRM). This is typically linked to the creditworthiness of the issuer or obligor and is often based on external credit ratings, such as those from S&P, Moody’s or Fitch. The final SRI is determined by combining MRM and CRM using the prescribed regulatory matrix.

Source: Commission Delegated Regulation (EU) 2017/653

Performance scenarios are another key element of the KID. These scenarios – stress, unfavourable, moderate and favourable – illustrate how a product may perform under different market conditions. They must be recalculated on a monthly basis using prescribed methodologies. While outcomes do not always change significantly from month to month, the requirement ensures the figures remain aligned with the most recent market data.

Costs also play an important role. The KID shows the reduction in return caused by costs over time, based on standardised assumptions. This includes both direct costs (such as entry or management fees) and indirect costs, such as transaction costs. The methodology also reflects the opportunity cost of these fees, meaning the impact of returns that could have been generated if those costs had not been incurred.

Towards a structured workflow approach

KID requirements demand consistent application of methodology, regular updates and full reproducibility. With increasing regulatory focus, this has become essential.

At Amsshare, we have embedded the full PRIIPs methodology into a structured workflow that goes beyond calculation. KID indicators are updated within seconds and automatically interpreted, highlighting key changes, risk movements and points of attention. This ensures compliance while directly supporting decision-making.

You can read more here about how these workflows are developed.

Curious how to stay compliant without manual effort? Feel free to get in touch.

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How to perform Liquidity Stress Testing under ESMA rules?

Sven Peter & Thomas — Mon, 20 Apr 2026 13:27:29 +0000

Estimated read time: 8 minutes

Liquidity Stress Testing (LST) has been a core regulatory requirement for asset managers since the introduction of the ESMA Guidelines in 2020. It plays a key role in assessing whether a fund can meet its obligations under both normal and stressed conditions.

With the introduction of new Liquidity Management Tool (LMT) regulations in April 2026, the importance of LST has increased significantly. These regulations explicitly rely on LST as an input for selecting and calibrating appropriate liquidity management tools.

As a result, LST is no longer just a standalone risk management exercise. It has become a critical component in demonstrating robust liquidity management under heightened regulatory scrutiny, requiring a structured, consistent and reproducible approach.

The importance of Liquidity Stress Testing

From a regulatory perspective, the purpose of LST is to ensure a consistent and harmonised approach to liquidity risk management across the European market. The ESMA guidelines aim to increase the standard, consistency and frequency of LST, ensuring that funds are assessed in a comparable and robust manner.

For asset managers, this translates into two key questions:

How liquid is the fund under normal conditions?
How does that liquidity change under stressed conditions?

Answering these questions requires a structured approach that considers the liquidity of the asset side, the behaviour of the liability side and the combined effect of both on the fund’s overall liquidity position.

LST is expected to produce clear, measurable indicators that provide a solid understanding of the fund’s liquidity risk, highlight potential vulnerabilities and define thresholds at which liquidity pressure may arise. These insights should directly support decision-making, for example by identifying when action is required, how liquidity can be improved or which liquidity management tools may need to be applied.

A practical starting point

A practical starting point for Liquidity Stress Testing is an assessment of the fund under normal conditions. This means understanding how the fund’s liquidity behaves in its current state, taking into account its key characteristics such as investment strategy, dealing frequency and structural constraints.

A comprehensive view of liquidity typically considers three core dimensions:

Assets
The composition of the portfolio, concentration of positions and the inherent liquidity of underlying assets determine how easily the portfolio can be liquidated under normal market conditions.
Liabilities
The structure of the investor base, including concentration and type of investors, provides insight into potential redemption pressure and how liquidity risk may materialise.
Fund dynamics
Historical patterns in subscriptions and redemptions, as well as the development of NAV and units over time, provide context on what constitutes normal inflow and outflow behaviour.

Designing the LST framework

The initial assessment under normal conditions highlights where potential risks may arise, which in turn informs how LST should be applied. This includes identifying relevant risk factors across both the asset and liability side, determining which scenarios are meaningful and how severe they should be.

In practice, this means defining:

The key liquidity risk drivers of the fund
The types of scenarios to be applied and the assumptions underpinning them
The outputs and indicators used to monitor liquidity risk over time
How results are reported and embedded into decision-making across risk, portfolio management and senior management

LST is expected to produce actionable insights that support follow-up actions, whether that is adjusting the portfolio, setting internal limits or preparing for periods of stress.

These elements should be formalised within an LST policy as part of the broader risk management framework. This policy captures governance, roles and responsibilities, reporting and escalation procedures, as well as key modelling choices such as scenario design, assumptions, liquidation approaches and testing frequency.

The frequency of LST is not fixed, but should reflect the nature, scale and liquidity profile of the fund. While annual testing is a minimum requirement, more frequent testing—such as quarterly—is often appropriate.

Defining stress scenarios

Stress scenarios should be severe but plausible and reflect a range of conditions that may impact the fund’s liquidity. Rather than relying on a single perspective, LST should incorporate multiple types of scenarios that together cover the full balance sheet.

Historical scenarios
Scenarios based on past market events, such as the global financial crisis or COVID-19, used to replicate how markets and investor behaviour evolved during those periods.
Hypothetical scenarios
Forward-looking scenarios reflecting potential risks, such as changes in macroeconomic conditions (e.g. interest rates or credit spreads) or idiosyncratic events affecting the fund, such as reputational issues or fraud.
Reverse stress testing
Scenarios constructed by identifying the conditions under which the fund would no longer be able to meet its obligations, and working backwards to determine what combination of events could lead to such an outcome.

Source: CBOE via FRED. Market volatility (VIX) over time, illustrating historical stress periods.

Each scenario is translated into a set of assumptions affecting both sides of the balance sheet. On the asset side, this may include changes in market conditions, trading volumes or pricing. On the liability side, it may involve shifts in investor behaviour, such as increased redemption activity or concentration effects.

This translation step is essential, as it determines how a scenario is applied within the stress testing framework. Assumptions should remain realistic and, where uncertainty exists, conservative. In particular, managers should avoid overly optimistic assumptions—for example, assuming that assets can be liquidated at full average daily volume without empirical support.

Stress testing the balance sheet

The defined scenarios are applied to the fund’s balance sheet to assess their impact on overall liquidity. This assessment considers both sides of the balance sheet individually, as well as their combined effect.

On the asset side, the focus lies on the ability to liquidate positions under both normal and stressed conditions. This typically includes:

Time to liquidate positions under varying market conditions
Liquidation cost, driven by asset type, trade size and execution horizon
Feasibility of liquidation, taking into account investment policy, risk profile and investor interests

Market conditions play a key role in this context. Under stress, reduced liquidity, wider bid-ask spreads and increased volatility may significantly affect both the feasibility and cost of liquidation. Assumptions should therefore reflect realistic execution conditions and avoid overly optimistic expectations, particularly for less liquid assets where pricing and market depth may be limited.

On the liability side, the focus is on potential outflows and other obligations that may put pressure on liquidity. This includes:

Redemptions, typically the primary driver of liquidity risk
Investor structure, such as concentration and investor type
Behavioural patterns, reflecting how investors may respond under different scenarios

In addition, other balance sheet obligations may become relevant depending on the fund’s structure. This includes, for example, margin calls on derivatives, committed capital requirements, obligations arising from securities financing transactions or changes in interest and credit conditions. These elements can materially impact available liquidity and should be reflected where appropriate.

The interaction between assets and liabilities is ultimately what determines liquidity risk. Reduced ability to liquidate assets combined with increased outflows or obligations can lead to liquidity pressure, particularly in stressed environments. A combined view of both sides of the balance sheet therefore provides the most complete picture of the fund’s resilience.

Towards a structured workflow approach

LST is a multi-step process that needs to be repeated periodically while maintaining consistency in methodology and assumptions. This makes it particularly well-suited for a structured workflow approach.

By embedding LST into such a workflow, managers can ensure automated updates based on current portfolio data and outputs that remain consistent, comparable and directly usable for decision-making.

At Amsshare, we embed the full LST process into a structured workflow, enabling managers to perform updates in seconds while maintaining full regulatory compliance and strengthening ongoing liquidity risk management.

You can read more here about how these workflows are developed.

Curious how to stay compliant without manual effort? Feel free to get in touch.

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How Fund Managers Should Approach ESMA’s New LMT Guidelines

Sven Peter & Thomas — Tue, 03 Jun 2025 11:54:52 +0000

Estimated read time: 6 minutes

What you need to know

The European Securities and Markets Authority (ESMA) has introduced new guidelines that require UCITS and AIFMs to take a much more practical and fund-specific approach to choosing Liquidity Management Tools (LMTs). The aim is simple: improve how funds manage liquidity risks and reduce the chances of market disruption, especially during periods of stress.

If you’re a fund manager or risk professional, here’s the key message: picking your LMTs means carefully assessing your fund’s characteristics, liquidity stress testing results and other metrics before deciding which tools to apply.

At Amsshare, we help fund managers implement the new LMT guidelines, from liquidity stress testing and fund analysis to selecting and calibrating the right tools. Below, we outline a practical approach aligned with ESMA’s latest expectations.

Step 1. Know the required types of LMTs

Every fund must now have at least two LMTs in place:

1. One quantitative-based LMT, such as:

Redemption gates – Limit how much investors can redeem during a certain period.
Extension of notice periods – Require investors to tell you earlier (“give more notice”) if they want to take their money out.
Suspension – Temporarily stop new investments (subscriptions), paying out investors who want to leave (redemptions) or buying back fund units (repurchases).
Redemption in Kind (RiK) – Investors are paid in underlying assets instead of cash. Often used in real estate or private equity funds.

2. One Anti-Dilution Tool (ADT), designed to protect existing investors from the negative effects of others entering or exiting the fund, such as:

Redemption fees – A charge for exiting the fund, often used to discourage short-term trades.
Swing pricing – Adjusts the NAV depending on cash flows, so costs the costs are paid by the investors who are buying or selling.
Dual pricing – Uses two different prices: a higher price (offer price) for investors entering the fund, and a lower price (bid price) for those exiting, based on dealing costs.
Anti-Dilution Levy (ADL) – A one-time charge added when investors enter or exit the fund. It’s different from a redemption fee because the amount stays in the fund to cover trading costs and protect existing investors from dilution.
Side pockets – Illiquid assets are set aside in a separate portion of the fund, so they don’t affect daily liquidity. Useful when certain assets become untradeable or are impacted by sanctions, defaults, etc.

The idea is that LMTs are not just emergency brakes, but everyday steering mechanisms. They help funds operate smoothly during redemptions, investor inflows or changes in market conditions. Choosing the right combination of LMTs can reduce forced selling and protect remaining investors.

Step 2. Analyse your fund in detail

ESMA wants fund managers to choose LMTs based on a good understanding of their fund. The tools should work well in both normal and stressed market conditions. To make strong and reliable choices, managers need to think about many different situations. According to ESMA, at least the following factors must be considered:

Legal Structure – For example, ETFs and master-feeder structures may have limits on which LMTs can work in practice.
Investment Strategy – A fund investing in liquid equities has different needs than one focused on private markets. Liquidity management should reflect the nature and volatility of the assets.
Dealing Terms – This includes redemption frequency, notice periods, lock-ups and settlement timelines. A fund with daily dealing might need a different toolkit than a fund that only allows redemptions monthly.
Liquidity of Assets – How easily can your fund sell its assets if many investors want to exit at the same time? This is a key part of understanding your fund’s liquidity profile. Large or sudden redemptions can create serious pressure, especially if the assets are hard to sell quickly. You should also consider how other cash demands, like margin calls, could add to this pressure. And importantly, think about how activating LMTs might affect your fund’s ability to meet these outflows during both normal and stressed market conditions.
Liquidity Stress Testing Results – These should already be part of your existing regulatory setup under AIFMD or UCITS. Refer to the ESMA Guidelines on Liquidity Stress Testing (LST) for further direction. The new LMT guidelines build on that foundation, asking you to link LST outcomes directly to your LMT setup.
Investor Base – Are your investors mainly retail or institutional? These groups behave differently under stress — institutional investors may redeem large amounts in one go, while retail flows can be more erratic. Also consider concentration risk: if a small number of investors hold a large share of the fund, even a few redemptions could create significant liquidity pressure.
Distribution Policy – Does your fund regularly pay out dividends? That can create added liquidity strain, especially in tight markets.
Operational Barriers – Some LMTs may be technically hard to implement depending on your systems or service providers. A good idea on paper might not be feasible in practice.

Did you know? Liquidity Stress Testing (LST) has already been a regulatory requirement under the ESMA Guidelines on Liquidity Stress Testing (LST)

These new LMT guidelines shine an even brighter light on LST, making it a mandatory input for selecting and calibrating liquidity management tools. It reinforces the importance of having a robust and fund-specific stress testing framework in place.

At Amsshare, we support fund managers in designing and implementing LST processes that meet ESMA’s expectations, and we help build on that foundation to select and calibrate LMTs under the latests guidance.

Step 3. Match LMTs to your fund's needs

ESMA’s guidance is clear: your selection and calibration of tools must be directly connected to the real characteristics and risks of your fund. This isn’t about picking tools from a checklist, it’s about building a liquidity framework that actually fits.

Here’s how different elements of your analysis should inform your decisions:

Dealing terms: If your fund offers daily redemptions, you may need tools that can activate quickly and frequently, like swing pricing or anti-dilution levies. For monthly or quarterly dealing, tools like notice periods or gates might be more appropriate.
Liquidity of assets: If your portfolio includes hard-to-sell positions — for example, private credit, real estate or small caps — you’ll likely need more robust tools, such as redemption in kind or the option to suspend redemptions under stress.
Liquidity stress testing results: These tests should reveal where your fund is vulnerable. For example, if stress scenarios show high outflow sensitivity, that might justify calibrating redemption gates or extending notice periods. The logic behind this connection should be clearly documented.

Each tool you choose, and how you set its parameters, should connect back to your analysis. Even when a decision seems obvious or aligns with market practice, it still needs to be backed up with evidence. That’s the essence of good risk management: not just highlighting where things could go wrong, but also showing why the risks are well understood and controlled.

Step 4. Define when to activate the tools

Having the tools in place is a good start, but it’s just as important to be clear about when and how you’ll actually use them. That might include:

Setting thresholds from your stress tests
Monitoring daily or weekly flow data
Having escalation procedures in place

Defining activation thresholds in advance helps reduce confusion and protects fund governance during stressful situations. It’s also helpful to run internal simulations to test readiness. Should swing pricing kick in at 1% of AUM outflow? Should a gate apply at 10%? These decisions must be justified and periodically reviewed.

Sometimes, combining tools makes more sense, like using both swing pricing and redemption gates during periods of heavy outflows.

Final thoughts

These ESMA guidelines are not just about compliance, they’re about being prepared. Having the right tools in place, and knowing when and how to use them, makes your fund more robust.

LMTs are now a central part of a fund’s liquidity risk framework. Choosing and calibrating them properly is not a one-time exercise, it’s an ongoing responsibility. By approaching the selection process methodically and documenting your reasoning, you’re not only protecting your fund from shocks, but also building trust with investors and regulators.

If you’re unsure where to start, Amsshare can help you review your fund setup, run pre-selection assessments and document everything regulators may ask for.

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Guidelines on LMTs of UCITS and open-ended AIFs

Draft RTS on LMTs under AIFMD and UCITS Directive

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How do banks select macroeconomic drivers in IFRS 9 models?

Sven Peter & Thomas — Sun, 02 Mar 2025 11:24:16 +0000

Banks are required to estimate credit risk in a forward-looking manner, particularly under the IFRS 9 regulatory framework. A key component of this process is the calculation of Expected Credit Losses, where default probabilities need to reflect current and expected economic conditions rather than long-term averages.

In practice, this requires a transition from Through-the-Cycle probabilities of default to Point-in-Time estimates. A crucial step in this process is identifying which macroeconomic variables are most relevant for explaining changes in default rates. As there is no prescribed method for selecting these variables, different approaches are used across institutions.

At Amsshare, we work with these types of models in practice and therefore assess not only how they are implemented, but also how they can be improved. This paper presents a structured and data-driven approach to selecting macroeconomic variables, applied to European corporate default data.

Summary

This paper examines how banks can incorporate macroeconomic information into credit risk models under the IFRS 9 framework, with a specific focus on the transition from Through-the-Cycle to Point-in-Time probabilities of default.

A key challenge in this process is the selection of macroeconomic variables that explain movements in default rates. Since no standard methodology is prescribed, this paper introduces a data-driven approach based on the LASSO BIC method. This technique automatically selects the most relevant variables while balancing model complexity and explanatory power.

The results show that certain macroeconomic variables, such as interest rates and credit spreads, play a consistent role in explaining default rates over time. At the same time, the analysis demonstrates that the importance of these variables can change, highlighting the need for periodic re-evaluation rather than relying on a fixed model specification.

These findings are directly relevant for banks and financial institutions that are required to implement IFRS 9 models. They show that combining regulatory requirements with robust variable selection techniques can improve the reliability of credit risk estimates, while also providing a more structured and transparent modelling approach.

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How reliable are KID performance scenarios?

Sven Peter & Thomas — Thu, 26 Sep 2024 12:35:27 +0000

European fund managers are required to include performance scenarios in Key Information Documents (KIDs), following the methodology prescribed by ESMA. These scenarios provide investors with an indication of potential future returns and are therefore an important part of investment decision-making.

At Amsshare, we perform these calculations for fund managers as part of regulatory requirements. At the same time, we also examine the methodology more critically. This paper builds on our previous research by using more recent data, evaluating forecasts across three dimensions and analysing whether portfolio characteristics influence forecast performance.

Summary

This paper compares the ESMA methodology with a LASSO BIC Machine Learning model for predicting one-year future returns of European passive stock ETFs. The ESMA model relies solely on past return data, while the LASSO BIC model incorporates macroeconomic variables such as GDP, implied volatility, interest rates and inflation.

To evaluate forecast performance, three dimensions are used.

A forecast is considered unbiased when it does not systematically overestimate or underestimate returns, meaning that forecast errors are on average close to zero.
Accuracy reflects how close forecasts are to realised returns, where smaller errors indicate better performance.
Efficiency relates to whether all relevant available information is incorporated in the forecast. If not, there is still room to improve predictions.

The results show that the ESMA model produces forecasts that are unbiased, but inaccurate and inefficient. The LASSO BIC model produces more accurate forecasts, but these forecasts are biased and still inefficient. Comparative tests show that the LASSO BIC model is significantly more accurate and less inefficient than the ESMA model, despite producing higher bias.

The paper also finds that ETF characteristics matter. Larger ETFs initially produce more accurate forecasts, but this effect turns negative beyond a certain threshold. ETFs with higher liquidity risk produce less accurate but more unbiased forecasts, while ETFs with higher market risk generate more biased forecasts.

The paper concludes that a one size fits all approach to performance scenario forecasting is limited. Investors should carefully interpret ESMA based forecasts, while regulators may consider forecasting models that incorporate macroeconomic variables and account for portfolio specific characteristics.

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Real-life example: Adyen’s cost of equity

Sven Peter & Thomas — Thu, 23 Nov 2023 10:15:23 +0000

Estimating the cost of equity is a key step in financial analysis, particularly in valuation and investment decision-making. A central component in this process is beta, which measures how sensitive a company is to market movements. In practice, it is important to understand how beta is constructed, and how factors such as leverage influence the overall risk profile of a firm.

At Amsshare, we work with financial models in which concepts such as beta and cost of equity play a central role. As these measures are widely used in valuation and risk analysis, it is essential to understand how they are derived and applied. This article provides a clear example of how these concepts are used, based on Adyen’s 2022 financial data.

Summary

This article explains how the CAPM can be used in practice to estimate the cost of equity, with a focus on the role of beta as a measure of systematic risk. Beta captures how sensitive a company’s returns are to movements in the overall market.

A distinction is made between the unlevered beta, which reflects the underlying business risk, and the levered beta, which incorporates the additional impact of financial leverage. Understanding this difference is essential when comparing companies or estimating firm-specific risk.

To move from unlevered to levered beta, the Hamada Formula is applied. This approach shows how leverage affects a company’s risk profile and allows for a consistent way to translate industry-level data into firm-specific inputs.

The article demonstrates this process using a real-life example of Adyen in 2022. Based on sector data and financial statements, a levered beta of 1.12 is calculated. This beta is then used within the CAPM framework to estimate the company’s cost of equity.

The results show that Adyen’s cost of equity in 2022 is approximately 8.36%, illustrating how these concepts are applied in practice and how they contribute to valuation and risk assessment.

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How do derivatives help manage market risk?

Sven Peter & Thomas — Thu, 31 Aug 2023 10:25:45 +0000

Managing market risk is an important aspect of working with financial assets. Changes in underlying prices can have a significant impact on the value of investments, making it essential to understand how these risks can be measured and controlled. Derivatives provide a flexible way to adjust risk exposures without directly changing the underlying positions.

At Amsshare, we develop financial models that help quantify these sensitivities and translate them into practical risk management strategies. Concepts such as sensitivities and hedging play a key role in this process. This article provides an overview of how these concepts can be used to manage market risk in practice.

Summary

This article introduces key concepts used to manage market risk, with a focus on the Delta Greek and Delta Hedging. These concepts help quantify how sensitive values are to changes in underlying asset prices and are widely applied in financial modelling and risk management.

The Delta measures how the value of a derivative changes when the price of the underlying asset moves. This makes it a useful indicator of how exposed a position is to market movements. In practice, Delta can be interpreted as a measure of price sensitivity.

To manage this sensitivity, Delta Hedging can be applied. By combining positions in derivatives and underlying assets, it is possible to reduce or neutralise the impact of small price changes. This results in a more controlled risk exposure without the need to fully adjust the underlying investments.

An important aspect of Delta Hedging is that it is a dynamic process. As market conditions change, the sensitivity of positions also changes, meaning that hedging positions need to be adjusted over time. In practice, this is done at discrete intervals, taking into account trading costs and operational constraints.

The article concludes by highlighting how Delta and Delta Hedging form a practical foundation for managing market risk. These concepts are directly relevant to how Amsshare develops and implements financial models and hedging simulations to support clients in managing risk more effectively.

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Why investors don’t always act rationally

Sven Peter & Thomas — Tue, 04 Jul 2023 10:29:17 +0000

Decision-making under uncertainty is a central topic in finance, influencing how investors evaluate risk and return. Traditional financial theory assumes that individuals behave rationally and consistently when making decisions. However, empirical evidence suggests that real-world behaviour often deviates from these assumptions.

At Amsshare, we analyse financial models and behavioural patterns that influence investment decisions. Understanding the theoretical foundations of decision-making, as well as their limitations, is essential for interpreting market behaviour and improving financial modelling approaches. This article provides an overview of two key theories that explain how individuals make decisions under uncertainty.

Summary

This article provides an overview of two prominent psychological theories in finance: the Expected Utility Theory (EUT) and the Prospect Theory (PT). The EUT is a normative framework that describes how individuals should make decisions, based on rational assumptions and a set of core principles.

The EUT is built on four key axioms: completeness, transitivity, continuity, and independence. It assumes that individuals evaluate outcomes based on total wealth and exhibit risk aversion. However, empirical evidence shows that actual decision-making behaviour often deviates from these assumptions.

The Prospect Theory (PT), developed by Kahneman and Tversky, provides a more realistic, descriptive view of decision-making. Instead of focusing on total wealth, it evaluates outcomes in terms of gains and losses. The theory highlights three key behavioural patterns: risk aversion over gains, risk-seeking behaviour over losses, and loss aversion, where losses have a stronger impact than gains.

In addition, the PT accounts for the distortion of probabilities, where individuals tend to overweight low probabilities and underweight high probabilities. These behavioural insights explain why individuals often make decisions that differ from traditional rational models.

The article concludes by comparing both theories and showing that the Prospect Theory provides a more accurate framework for understanding real-world decision-making. These insights are relevant for interpreting investor behaviour and for developing more realistic financial models.

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Understanding Order Driven Markets

Sven Peter & Thomas — Wed, 24 May 2023 10:32:26 +0000

Financial markets rely on efficient transaction processing to ensure that buy and sell orders are executed in a timely and transparent manner. In practice, however, real-world markets are characterised by various frictions and inefficiencies that influence how trades are executed and how prices are formed.

At Amsshare, we develop and implement financial models that depend on accurate transaction data and a clear understanding of market structure. Insight into how transactions are processed and how different market mechanisms operate is therefore essential. This article provides a practical overview of how equity exchanges function and how orders are matched in modern markets.

Summary

This article introduces transaction processing on equity exchanges and highlights the frictions and inefficiencies present in real-world markets. It explains the two main types of market structures, quote-driven markets and order-driven markets, with a particular focus on the latter.

In an order-driven market, buyers and sellers submit orders to a central order book, where trades are matched based on price and time priority. The article discusses the concept of the bid-ask spread, the role of brokers as intermediaries and the importance of a continuous flow of orders for maintaining liquidity.

Two primary order types are examined: market orders and limit orders, both of which are stored and executed through a limit order book. The article also includes visual representations of theoretical and live order books to illustrate how these mechanisms function in practice.

The article concludes by highlighting the advantages of order-driven markets, including increased transparency, lower costs compared to quote-driven markets and greater flexibility in order execution. These insights are directly relevant to how Amsshare analyses market behaviour and develops financial models that rely on accurate transaction data.

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How do you price a derivative?

Sven Peter & Thomas — Thu, 04 May 2023 10:34:41 +0000

Derivatives play a fundamental role in modern financial markets, offering investors tools to manage risk, enhance returns and structure complex investment strategies. Among these instruments, options are particularly important due to their flexibility and wide range of applications.

At Amsshare, we frequently work with financial models that incorporate derivatives and option pricing techniques. Given their importance in both regulatory and investment contexts, we aim to provide a clear and practical overview of how these instruments work and how their valuation is approached in theory and practice.

Summary

This article provides an overview of common derivatives used in financial markets and their respective applications. It introduces the core mechanism of option pricing, known as the rule of no arbitrage, which ensures that prices remain consistent and free from riskless profit opportunities.

Two key methods for calculating option prices are discussed: the replicating portfolio method and the risk-neutral valuation method. Both approaches are explained from a theoretical perspective and supported with computational examples to illustrate their practical implementation.

The replicating portfolio method demonstrates how an option can be priced by constructing a portfolio that exactly replicates its payoff. In contrast, the risk-neutral valuation method simplifies the process by valuing expected future payoffs under a risk-neutral probability measure.

The article concludes by highlighting the practical advantages of the risk-neutral valuation method, particularly in terms of scalability and efficiency in real-world applications. These insights are directly relevant to how Amsshare develops and implements financial models for clients, ensuring both accuracy and computational effectiveness.

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