Optimal Dynamic Hedging Strategies with Financial Futures Contracts Using Nonlinear Conditional Heteroskedastic Models PDF Download
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Author: ANTHONY TUCK-KWAI CHAN
Publisher:
ISBN:
Category : Financial futures
Languages : en
Pages : 402
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Book Description
Treasury bills futures market are chosen for the purpose of empirical study.
Author: ANTHONY TUCK-KWAI CHAN
Publisher:
ISBN:
Category : Financial futures
Languages : en
Pages : 402
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Book Description
Treasury bills futures market are chosen for the purpose of empirical study.
Author: Su Dai
Publisher: GRIN Verlag
ISBN: 3656539219
Category : Business & Economics
Languages : en
Pages : 80
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Book Description
Master's Thesis from the year 2013 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,7, University of Mannheim, language: English, abstract: The commodity futures contract is an agreement to deliver a specific amount of commodity at a future time . There are usually choices of deliverable grades, delivery locations and delivery dates. Hedging belongs to one of the fundamental functions of futures market. Futures can be used to help producers and buyers protect themselves from price risk arising from many factors. For instance, in crude oil commodities, price risk occurs due to disrupted oil supply as a consequence of political issues, increasing of demand in emerging markets, turnaround in energy policy from the fossil fuel to the solar and efficient energy, etc. By hedging with futures, producers and users can set the prices they will receive or pay within a fixed range. A hedger takes a short position if he/she sells futures contracts while owning the underlying commodity to be delivered; a long position if he/she purchases futures contracts. The commonly known basis is defined as the difference between the futures and spot prices, which is mostly time-varying and mean-reverting. Due to such basis risk, a naïve hedging (equal and opposite) is unlikely to be effective. With the popularity of commodity futures, how to determine and implement the optimal hedging strategy has become an important issue in the field of risk management. Hedging strategies have been intensively studied since the 1960s. One of the most popular approaches to hedging is to quantify risk as variance, known as minimum-variance (MV) hedging. This hedging strategy is based on Markowitz portfolio theory, resting on the result that “a weighted portfolio of two assets will have a variance lower than the weighted average variance of the two individual assets, as long as the two assets are not perfectly and positively correlated.” MV strategy is quite well accepted, however, it ignores the expected return of the hedged portfolio and the risk preference of investors. Other hedging models with different objective functions have been studied intensively in hedging literature. Due to the conceptual simplicity, the value at risk (VaR) and conditional value at risk (C)VaR have been adopted as the hedging risk objective function. [...]
Author: Akash Deep
Publisher:
ISBN:
Category :
Languages : en
Pages : 33
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Book Description
Both financial and non-financial firms routinely implement hedging policies to mitigate their exposure to changes in asset prices. However, while these policies may perform satisfactorily in the limited sense of hedging the exposure under consideration, they might increase the overall likelihood of financial distress due to the liquidity risks that they create. This paper examines the case of hedging price risk using derivative contracts that are marked to market (such as futures contracts) and hence subject to margin calls. It is shown that liquidity risk, stemming from the need to meet margin calls on the futures position, can be a significant source of risk and can even lead to financial distress even though the firm remains quot;hedgedquot;. Such risks should therefore be taken into account in the formulation of an optimal hedging policy. This paper derives the dynamic hedging strategy of a firm that uses futures contracts to hedge a spot market exposure. The risk emanating from the margin requirement on futures contracts is incorporated into the hedging decision by restricting the borrowing capacity of the firm. It is shown that this leads to a substantial reduction in the firm's optimal hedge, especially if the hedging horizon is long. The results provide theoretical support for the low level of hedging observed empirically.
Author: Kenneth F. Kroner
Publisher:
ISBN:
Category : Financial futures
Languages : en
Pages : 44
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Book Description
Author: Chih-Chiang Hsu
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Book Description
In a number of prior studies it has been demonstrated that the traditional regression-based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies has emerged. In this paper we propose a class of new copula-based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. In regards to the reduction of variance in the returns of hedged portfolios, our empirical results show that in both the in-sample and out-of-sample tests, with full flexibility in the distribution specifications, the copula-based GARCH models perform more effectively than other dynamic hedging models.
Author: Gregory Koutmos
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This article proposes a dynamic hedging model for Government National Association Mortgage-Backed Securities (GNMA MBSs) that is free of the drawbacks associated with the static hedging strategies currently used. The simultaneity bias of the regression approach is dealt with by modeling the joint distribution of price changes of GNMA MBSs and 10-year Treasury-note futures. Error correction (EC) terms from cointegrating relationships are included in the conditional mean equations to preserve the long-term equilibrium relationship of the two markets. The time-varying variance-covariance structure of the two markets is modeled via a version of the bivariate generalized autoregressive conditionally heteroskedastic model (bivariante GARCH), which assures that the time-varying variance-covariance matrix is positive semidefinite for all time periods. This dynamic error-correction GARCH model is estimated using daily data on six different coupon GNMA MBSs. Dynamic cross-hedge ratios are obtained from the time-varying variance-covariance matrix using the 10-year Treasury-note futures contract as the hedging instrument. These ratios are evaluated in terms of both overall risk reduction and expected utility maximization. There is overwhelming evidence that dynamic hedge ratios are superior to static ones even when transaction costs are incorporated into the analysis. This conclusion holds for all six different coupon GNMA MBSs under investigation.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 592
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Author: Johan Hagenbjörk
Publisher: Linköping University Electronic Press
ISBN: 917929927X
Category :
Languages : sv
Pages : 129
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Book Description
The global fixed income market is an enormous financial market whose value by far exceeds that of the public stock markets. The interbank market consists of interest rate derivatives, whose primary purpose is to manage interest rate risk. The credit market primarily consists of the bond market, which links investors to companies, institutions, and governments with borrowing needs. This dissertation takes an optimization perspective upon modeling both these areas of the fixed-income market. Legislators on the national markets require financial actors to value their financial assets in accordance with market prices. Thus, prices of many assets, which are not publicly traded, must be determined mathematically. The financial quantities needed for pricing are not directly observable but must be measured through solving inverse optimization problems. These measurements are based on the available market prices, which are observed with various degrees of measurement noise. For the interbank market, the relevant financial quantities consist of term structures of interest rates, which are curves displaying the market rates for different maturities. For the bond market, credit risk is an additional factor that can be modeled through default intensity curves and term structures of recovery rates in case of default. By formulating suitable optimization models, the different underlying financial quantities can be measured in accordance with observable market prices, while conditions for economic realism are imposed. Measuring and managing risk is closely connected to the measurement of the underlying financial quantities. Through a data-driven method, we can show that six systematic risk factors can be used to explain almost all variance in the interest rate curves. By modeling the dynamics of these six risk factors, possible outcomes can be simulated in the form of term structure scenarios. For short-term simulation horizons, this results in a representation of the portfolio value distribution that is consistent with the realized outcomes from historically observed term structures. This enables more accurate measurements of interest rate risk, where our proposed method exhibits both lower risk and lower pricing errors compared to traditional models. We propose a method for decomposing changes in portfolio values for an arbitrary portfolio into the risk factors that affect the value of each instrument. By demonstrating the method for the six systematic risk factors identified for the interbank market, we show that almost all changes in portfolio value and portfolio variance can be attributed to these risk factors. Additional risk factors and approximation errors are gathered into two terms, which can be studied to ensure the quality of the performance attribution, and possibly improve it. To eliminate undesired risk within trading books, banks use hedging. Traditional methods do not take transaction costs into account. We, therefore, propose a method for managing the risks in the interbank market through a stochastic optimization model that considers transaction costs. This method is based on a scenario approximation of the optimization problem where the six systematic risk factors are simulated, and the portfolio variance is weighted against the transaction costs. This results in a method that is preferred over the traditional methods for all risk-averse investors. For the credit market, we use data from the bond market in combination with the interbank market to make accurate measurements of the financial quantities. We address the notoriously difficult problem of separating default risk from recovery risk. In addition to the previous identified six systematic risk factors for risk-free interests, we identify four risk factors that explain almost all variance in default intensities, while a single risk factor seems sufficient to model the recovery risk. Overall, this is a higher number of risk factors than is usually found in the literature. Through a simple model, we can measure the variance in bond prices in terms of these systematic risk factors, and through performance attribution, we relate these values to the empirically realized variances from the quoted bond prices. De globala ränte- och kreditmarknaderna är enorma finansiella marknader vars sammanlagda värden vida överstiger de publika aktiemarknadernas. Räntemarknaden består av räntederivat vars främsta användningsområde är hantering av ränterisker. Kreditmarknaden utgörs i första hand av obligationsmarknaden som syftar till att förmedla pengar från investerare till företag, institutioner och stater med upplåningsbehov. Denna avhandling fokuserar på att utifrån ett optimeringsperspektiv modellera både ränte- och obligationsmarknaden. Lagstiftarna på de nationella marknaderna kräver att de finansiella aktörerna värderar sina finansiella tillgångar i enlighet med marknadspriser. Därmed måste priserna på många instrument, som inte handlas publikt, beräknas matematiskt. De finansiella storheter som krävs för denna prissättning är inte direkt observerbara, utan måste mätas genom att lösa inversa optimeringsproblem. Dessa mätningar görs utifrån tillgängliga marknadspriser, som observeras med varierande grad av mätbrus. För räntemarknaden utgörs de relevanta finansiella storheterna av räntekurvor som åskådliggör marknadsräntorna för olika löptider. För obligationsmarknaden utgör kreditrisken en ytterligare faktor som modelleras via fallissemangsintensitetskurvor och kurvor kopplade till förväntat återvunnet kapital vid eventuellt fallissemang. Genom att formulera lämpliga optimeringsmodeller kan de olika underliggande finansiella storheterna mätas i enlighet med observerbara marknadspriser samtidigt som ekonomisk realism eftersträvas. Mätning och hantering av risker är nära kopplat till mätningen av de underliggande finansiella storheterna. Genom en datadriven metod kan vi visa att sex systematiska riskfaktorer kan användas för att förklara nästan all varians i räntekurvorna. Genom att modellera dynamiken i dessa sex riskfaktorer kan tänkbara utfall för räntekurvor simuleras. För kortsiktiga simuleringshorisonter resulterar detta i en representation av fördelningen av portföljvärden som väl överensstämmer med de realiserade utfallen från historiskt observerade räntekurvor. Detta möjliggör noggrannare mätningar av ränterisk där vår föreslagna metod uppvisar såväl lägre risk som mindre prissättningsfel jämfört med traditionella modeller. Vi föreslår en metod för att dekomponera portföljutvecklingen för en godtycklig portfölj till de riskfaktorer som påverkar värdet för respektive instrument. Genom att demonstrera metoden för de sex systematiska riskfaktorerna som identifierats för räntemarknaden visar vi att nästan all portföljutveckling och portföljvarians kan härledas till dessa riskfaktorer. Övriga riskfaktorer och approximationsfel samlas i två termer, vilka kan användas för att säkerställa och eventuellt förbättra kvaliteten i prestationshärledningen. För att eliminera oönskad risk i sina tradingböcker använder banker sig av hedging. Traditionella metoder tar ingen hänsyn till transaktionskostnader. Vi föreslår därför en metod för att hantera riskerna på räntemarknaden genom en stokastisk optimeringsmodell som också tar hänsyn till transaktionskostnader. Denna metod bygger på en scenarioapproximation av optimeringsproblemet där de sex systematiska riskfaktorerna simuleras och portföljvariansen vägs mot transaktionskostnaderna. Detta resulterar i en metod som, för alla riskaverta investerare, är att föredra framför de traditionella metoderna. På kreditmarknaden använder vi data från obligationsmarknaden i kombination räntemarknaden för att göra noggranna mätningar av de finansiella storheterna. Vi angriper det erkänt svåra problemet att separera fallissemangsrisk från återvinningsrisk. Förutom de tidigare sex systematiska riskfaktorerna för riskfri ränta, identifierar vi fyra riskfaktorer som förklarar nästan all varians i fallissemangsintensiteter, medan en enda riskfaktor tycks räcka för att modellera återvinningsrisken. Sammanlagt är detta ett större antal riskfaktorer än vad som brukar användas i litteraturen. Via en enkel modell kan vi mäta variansen i obligationspriser i termer av dessa systematiska riskfaktorer och genom prestationshärledningen relatera dessa värden till de empiriskt realiserade varianserna från kvoterade obligationspriser.
Author: Charles M.S. Sutcliffe
Publisher: Routledge
ISBN: 1351148540
Category : Business & Economics
Languages : en
Pages : 844
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Book Description
The global value of trading in index futures is about $20 trillion per year and rising and for many countries the value traded is similar to that traded on their stock markets. This book describes how index futures markets work and clearly summarises the substantial body of international empirical evidence relating to these markets. Using the concepts and tools of finance, the book also provides a comprehensive description of the economic forces that underlie trading in index futures. Stock Index Futures 3/e contains many teaching and learning aids including numerous examples, a glossary, essay questions, comprehensive references, and a detailed subject index. Written primarily for advanced undergraduate and postgraduate students, this text will also be useful to researchers and market participants who want to gain a better understanding of these markets.
Author: Manolis G. Kavussanos
Publisher: Taylor & Francis
ISBN: 1000368963
Category : Transportation
Languages : en
Pages : 555
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Book Description
This advanced practical textbook deals with the issue of risk analysis, measurement and management in the shipping industry. It identifies and analyses the sources of risk in the shipping business and explores in detail the “traditional” and “modern” strategies for risk management at both the investment and operational levels of the business. The special features and characteristics of all available freight derivative products are compared and contrasted between them. Practical applications of derivatives are showcased through realistic practical examples, while a number of concepts across the contents of this book appear for the first time in the literature. The book also serves as “the reference” point for researchers in the area, helping them to enhance their knowledge of risk management and derivatives in the shipping industry, but also to students at both undergraduate and postgraduate levels. Finally, it provides a comprehensive manual for practitioners wishing to engage in the financial risk management of maritime business. This second edition has been fully updated in order to incorporate the numerous developments in the industry since its first edition in 2006. New chapters have been introduced on topics such as Market Risk Measurement, Credit Risk and Credit Derivatives, and Statistical Methods to Quantify Risk. Furthermore, the second edition of this book builds upon the successful first edition which has been extensively (i) taught in a number of Universities around the world and (ii) used by professionals in the industry. Shipowners, professionals in the shipping industry, risk management officers, credit officers, traders, investors, students and researchers will find the book indispensable in order to understand how risk management and hedging tools can make the difference for companies to remain competitive and stay ahead of the rest.
