Le Prix de Règlement en Livraison sur Marché (PRLM) est un élément crucial du monde financier, particulièrement concernant le règlement des contrats à terme. Bien que ce terme ne soit pas largement connu en dehors de certains cercles financiers, son rôle dans la garantie de la justesse et de l'efficacité des opérations de marché est significatif. Cet article vise à clarifier sa signification et ses implications.
Qu'est-ce que le PRLM ?
Le PRLM représente le prix final et convenu auquel un contrat à terme est réglé lorsque la livraison de l'actif sous-jacent est effectuée. Il est déterminé par la bourse sur laquelle le contrat est négocié et est généralement basé sur une moyenne pondérée des prix observés pendant une période spécifiée proche de la date d'expiration du contrat. Ceci contraste avec les contrats à terme réglés en espèces, où aucune livraison physique n'a lieu, et le règlement est basé sur le prix de l'actif sous-jacent à l'expiration.
Comment le PRLM est-il déterminé ?
La méthodologie précise de calcul du PRLM varie légèrement selon la bourse et le produit ou instrument spécifique négocié. Cependant, les éléments communs incluent :
Pourquoi le PRLM est-il important ?
Le PRLM remplit plusieurs fonctions critiques :
PRLM vs. Autres prix de règlement :
Il est crucial de distinguer le PRLM des autres prix de règlement, tels que le prix de règlement utilisé dans les contrats à terme réglés en espèces. Bien que tous deux concernent le prix final, le PRLM est spécifiquement lié à la livraison physique de l'actif sous-jacent, tandis que le règlement en espèces utilise un prix de référence déterminé différemment (souvent un cours de clôture ou une moyenne de prix).
En résumé :
Le Prix de Règlement en Livraison sur Marché est un élément clé du mécanisme du marché à terme, garantissant un règlement juste et efficace des contrats impliquant une livraison physique. Sa méthodologie de calcul est conçue pour fournir un prix robuste et fiable, minimisant les risques et améliorant l'intégrité du marché. Bien qu'il ne soit pas un terme familier, son importance au sein de l'industrie financière est indéniable.
Instructions: Choose the best answer for each multiple-choice question.
1. What is the Exchange Delivery Settlement Price (EDSP)? (a) The price at which a futures contract is bought or sold. (b) The final agreed-upon price for a cash-settled futures contract. (c) The final agreed-upon price at which a futures contract is settled when delivery of the underlying asset is made. (d) The average price of a commodity over a month.
(c) The final agreed-upon price at which a futures contract is settled when delivery of the underlying asset is made.
2. The EDSP is primarily used in which type of futures contract? (a) Cash-settled futures contracts (b) Futures contracts involving physical delivery of the underlying asset (c) Index futures contracts (d) All types of futures contracts
(b) Futures contracts involving physical delivery of the underlying asset
3. Which of the following is NOT typically a factor in determining the EDSP? (a) The final trading session's prices (b) An averaging period near the contract's expiry (c) The closing price of the underlying asset a week before expiry (d) A weighting scheme for prices within the averaging period
(c) The closing price of the underlying asset a week before expiry
4. What is a key benefit of using the EDSP? (a) Increased price volatility for market participants (b) Reduced transparency in the settlement process (c) Fair and transparent settlement for buyers and sellers (d) Increased complexity in the delivery process
(c) Fair and transparent settlement for buyers and sellers
5. How does the EDSP compare to settlement prices in cash-settled futures contracts? (a) They are identical. (b) The EDSP is only used for physical delivery, while cash-settled futures use a different reference price. (c) The EDSP is more volatile. (d) The EDSP is less transparent.
(b) The EDSP is only used for physical delivery, while cash-settled futures use a different reference price.
Scenario: A copper futures contract is set to expire. The exchange uses the last hour of trading to determine the EDSP. The prices (per pound) observed in this hour are as follows:
For simplicity, let's assume a simple average is used to calculate the EDSP. What is the EDSP for this copper contract?
To calculate the simple average, we sum the prices and divide by the number of price points:
(4.00 + 4.05 + 4.02 + 4.03 + 4.04) / 5 = 4.028
Therefore, the EDSP for this copper contract is $4.03 (rounded to the nearest cent).
"futures contract settlement" -cash (to exclude cash-settled contracts)"commodity futures" "delivery price" site:.gov (to find government resources)"physical delivery" "futures pricing" filetype:pdf (to find PDFs of relevant documents)The calculation of the Exchange Delivery Settlement Price (EDSP) employs various techniques to ensure fairness and accuracy. While specific methodologies vary across exchanges and underlying assets, several common techniques are employed:
1. Weighted Average Pricing: This is the most prevalent technique. Prices from a specific period (typically the final trading session) are averaged, but not equally. Prices closer to the end of the trading period often receive higher weights, reflecting the most current market sentiment. The weighting scheme itself can be complex, potentially incorporating factors like trading volume or price volatility at specific intervals.
2. Volume-Weighted Average Price (VWAP): This method assigns weights to prices based on the trading volume at each price point. A price with high trading volume receives a higher weight, reflecting its greater market significance. VWAP helps to filter out potentially erratic price movements driven by small trades.
3. Time-Weighted Average Price (TWAP): This method assigns equal weights to prices observed across a specified time interval. It's simpler than VWAP but might be more susceptible to manipulation by large, single trades if the averaging window is too short.
4. Arithmetic Mean: A straightforward approach where all prices within the selected time window are summed and then divided by the number of prices. This is less common for EDSP calculation due to its susceptibility to outliers.
5. Median Price: This approach selects the middle value from a sorted list of prices. It is less sensitive to extreme values (outliers) than the arithmetic mean, making it robust to potential market manipulation attempts.
Data Smoothing Techniques: To minimize the impact of short-term price fluctuations, exchanges may employ data smoothing techniques, such as moving averages (e.g., simple moving average, exponential moving average) before applying the averaging method.
Choice of Technique: The selection of the optimal technique depends on several factors, including the specific characteristics of the underlying asset, the desired level of sensitivity to price fluctuations, and the exchange's regulatory requirements. The goal is always to strike a balance between accuracy, fairness, and robustness against manipulation.
Several underlying models inform the practical techniques used for EDSP calculation. While not explicitly stated as separate “models,” these conceptual frameworks guide the selection and application of the chosen technique:
1. The Fair Value Model: This underlying principle aims to establish a settlement price that reflects the true market value of the underlying asset at the time of contract expiry. The chosen averaging technique and weighting scheme should strive to approximate this fair value, minimizing the influence of short-term market noise.
2. The Risk Mitigation Model: This model focuses on reducing the risk of price manipulation and ensuring fair treatment for both buyers and sellers. The choice of averaging period and weighting scheme, as well as the inclusion of data smoothing techniques, is driven by the need to minimize the impact of potential manipulative activities.
3. The Transparency and Integrity Model: This emphasizes the importance of a clear, well-defined, and publicly accessible methodology for EDSP calculation. This increases market confidence and trust, reducing the potential for disputes and enhancing the integrity of the futures market.
4. The Operational Efficiency Model: This model highlights the need for a computationally efficient and practical methodology that can be implemented in real-time or near real-time. The complexity of the calculation must be balanced against the need for timely settlement.
These models are not mutually exclusive; rather, they represent considerations that inform the design and implementation of the EDSP calculation process. The ultimate goal is to create a system that is both accurate and practically feasible.
The calculation of the EDSP is typically handled by sophisticated software systems within the exchange's infrastructure. These systems perform several crucial functions:
1. Data Acquisition and Cleaning: Real-time market data feeds from various sources are integrated and cleaned to remove erroneous or irrelevant data points. This ensures the accuracy and reliability of the inputs to the EDSP calculation.
2. Price Averaging and Weighting: The core of the software is the implementation of the selected averaging and weighting techniques. This often involves custom algorithms designed to handle high volumes of data efficiently and accurately.
3. Data Validation and Error Handling: Robust error handling mechanisms are crucial to prevent incorrect EDSP calculations. The system must identify and handle potential data errors and exceptions gracefully.
4. Reporting and Audit Trails: Detailed audit trails of the EDSP calculation process are maintained for transparency and regulatory compliance. This allows for scrutiny and verification of the results.
5. Integration with Clearing and Settlement Systems: The EDSP is automatically integrated with the exchange's clearing and settlement systems to ensure seamless processing of transactions.
Technology Stack: The technology stack used varies depending on the exchange and its infrastructure. However, common technologies include high-performance databases (e.g., Oracle, PostgreSQL), programming languages suitable for high-frequency trading (e.g., C++, Java), and specialized financial market data platforms. Cloud computing infrastructure is increasingly being used to handle the large data volumes and computational demands involved.
Several best practices ensure fairness, accuracy, and transparency in EDSP determination:
1. Clear and Publicly Available Methodology: The precise methodology used for EDSP calculation, including the averaging period, weighting scheme, and data sources, should be clearly documented and publicly available. This promotes transparency and reduces the potential for disputes.
2. Robust Data Validation and Error Handling: Rigorous data validation and error handling mechanisms are essential to prevent erroneous EDSP calculations. This includes checks for data integrity, outliers, and potential manipulation attempts.
3. Regular Audits and Reviews: Regular audits and reviews of the EDSP calculation process should be conducted by independent parties to ensure the continued accuracy and reliability of the system.
4. Use of Multiple Data Sources: Utilizing data from multiple reputable sources can reduce reliance on a single source and mitigate the risk of data manipulation or errors.
5. Independent Verification: An independent third-party verification of the EDSP calculation can significantly enhance market confidence and reduce the potential for disputes.
6. Timely Dissemination of Results: The EDSP should be disseminated to market participants promptly after the calculation is complete to facilitate timely settlement of futures contracts.
7. Adaptation to Market Conditions: The EDSP methodology should be regularly reviewed and adapted to changes in market conditions, such as increased volatility or the emergence of new trading strategies.
While specific details of EDSP calculations are often confidential due to competitive and regulatory reasons, we can illustrate the principles with hypothetical examples:
Case Study 1: Agricultural Commodity: Imagine an exchange trading corn futures contracts. The EDSP might be calculated as a volume-weighted average price of the last hour of trading on the contract's expiry day. High trading volume at specific price points would influence the final EDSP significantly. Data smoothing techniques might be used to reduce the impact of price spikes caused by late trading activity.
Case Study 2: Precious Metals: For gold futures, the EDSP calculation might involve a time-weighted average of prices during the final 30 minutes of trading, with equal weights assigned to each price observation. The emphasis on a time-weighted average could reflect a need to reduce the potential influence of large, late trades.
Case Study 3: Financial Instruments: For interest rate futures, the EDSP could use a complex weighting scheme incorporating data from multiple sources, reflecting different market participants and their influence on pricing.
These hypothetical case studies highlight the adaptability of EDSP calculation techniques to various market contexts and underlying assets. The specific methodology is always chosen to strike a balance between fairness, accuracy, and robustness against manipulation, reflecting the unique characteristics of each market. In each instance, transparency in the methodology employed is key.
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