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Derivative Securities (derivative + security)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	88%

Selected Abstracts

A Framework for Valuing Derivative Securities

FINANCIAL MARKETS, INSTITUTIONS & INSTRUMENTS, Issue 5 2001
Philip Gray
This paper develops a general framework for valuing a wide range of derivative securities. Rather than focusing on the stochastic process of the underlying security and developing an instantaneously-riskless hedge portfolio, we focus on the terminal distribution of the underlying security. This enables the derivative security to be valued as the weighted sum of a number of component pieces. The component pieces are simply the different payoffs that the security generates in different states of the world, and they are weighted by the probability of the particular state of the world occurring. A full set of derivations is provided. To illustrate its use, the valuation framework is applied to plain-vanilla call and put options, as well as a range of derivatives including caps, floors, collars, supershares, and digital options. [source]

Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS)

EUROPEAN FINANCIAL MANAGEMENT, Issue 1 2002
Giovanni Barone-Adesi
Filtered historical simulation provides the general framework to our backtests of portfolios of derivative securities held by a large sample of financial institutions. We allow for stochastic volatility and exchange rates. Correlations are preserved implicitly by our simulation procedure. Options are repriced at each node. Overall results support the adequacy of our framework, but our VaR numbers are too high for swap portfolios at long horizons and too low for options and futures portfolios at short horizons. [source]

A Framework for Valuing Derivative Securities

A neural network versus Black,Scholes: a comparison of pricing and hedging performances

JOURNAL OF FORECASTING, Issue 4 2003
Henrik Amilon
Abstract An Erratum has been published for this article in Journal of Forecasting 22(6-7) 2003, 551 The Black,Scholes formula is a well-known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black,Scholes formula. The neural network method is applied to the out-of-sample pricing and delta-hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black,Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level.,Copyright © 2003 John Wiley & Sons, Ltd. [source]

The Term Structure of Simple Forward Rates with Jump Risk

MATHEMATICAL FINANCE, Issue 3 2003
Paul Glasserman
This paper characterizes the arbitrage-free dynamics of interest rates, in the presence of both jumps and diffusion, when the term structure is modeled through simple forward rates (i.e., through discretely compounded forward rates evolving continuously in time) or forward swap rates. Whereas instantaneous continuously compounded rates form the basis of most traditional interest rate models, simply compounded rates and their parameters are more directly observable in practice and are the basis of recent research on "market models." We consider very general types of jump processes, modeled through marked point processes, allowing randomness in jump sizes and dependence between jump sizes, jump times, and interest rates. We make explicit how jump and diffusion risk premia enter into the dynamics of simple forward rates. We also formulate reasonably tractable subclasses of models and provide pricing formulas for some derivative securities, including interest rate caps and options on swaps. Through these formulas, we illustrate the effect of jumps on implied volatilities in interest rate derivatives. [source]

A Note on Finding the Optimal Allocation Between a Risky Stock and a Risky Bond

THE JOURNAL OF FUTURES MARKETS, Issue 12 2001
John E. Angus
The allocation of financial assets among securities with different levels of risk is an essential topic in the study, analysis, and strategic use of derivative securities and markets. In a recent paper, Browne (1999) determined the optimal allocation strategy for dividing investments between a risky stock and a risky bond. In this note, Browne's equation determining the optimal strategy is studied and some methods are described for solving it. In addition, some useful rules-of-thumb, computational methods, and approximation techniques are presented. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:1181,1196, 2001 [source]

Forecasting stock index volatility

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2001
Riccardo Bramante
Abstract Accurate volatility forecasting is the key to successful risk analysis. In fact, volatility forecasts lie at the centre of many financial systems, such as value at risk modelling and pricing of derivative securities. This paper is concerned with how to construct stock index volatility predictors using the returns histories of the stocks that define the Index. Specifically, our approach presupposes that the total volatility of the index returns can be explained by the volatility of the related components. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Information Effects of Trade Size and Trade Direction: Evidence from the KOSPI 200 Index Options Market,

ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 3 2010
Hee-Joon Ahn
G10; G13 Abstract In the present study, we examine two important issues related to the information content of a trade in option markets: (i) whether trade size is related to information content; and (ii) whether buy and sell transactions carry different information content. Our analysis is based on comprehensive market microstructure data on the KOSPI 200 options, the single most actively traded derivative securities in the world. We use two structural models modified from the Madhavan et al. [Review of Financial Studies 10 (1997) 1035,1064] model, the size-dependent model (SDM), and the dummy variable model (DVM). The SDM incorporates trade size in the model to estimate the magnitude of the information content of a trade. The DVM separately estimates information contents for buyer- and seller-initiated trades using a dummy variable. Our SDM analysis reveals that large trades are in general more informative than small trades. The results from the DVM analysis indicate that buyer-initiated trades generally have greater information content than seller-initiated trades. A further analysis using investor-type information shows that the asymmetry in information content between buy and sell trades is mostly attributable to the orders submitted by foreign and domestic institutional investors. [source]