Volatility spillovers for spot, futures, and ETF prices in energy and agriculture

The agricultural and energy industries are closely related, both biologically and financially. The paper discusses the relationship and the interactions on price and volatility, with special focus on the covolatility spillover effects for these two industries. The interaction and covolatility spillovers or the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset, between the energy and agricultural industries is the primary emphasis of the paper. Although there has already been significant research on biofuel and biofuel-related crops, much of the previous research has sought to find a relationship among commodity prices. Only a few published papers have been concerned with volatility spillovers. However, it must be emphasized that there have been numerous technical errors in the theoretical and empirical research, which needs to be corrected. The paper not only considers futures prices as a widely-used hedging instrument, but also takes an interesting new hedging instrument, ETF, into account. ETF is regarded as index futures when investors manage their portfolios, so it is possible to calculate an optimal dynamic hedging ratio. This is a very useful and interesting application for the estimation and testing of volatility spillovers. In the empirical analysis, multivariate conditional volatility diagonal BEKK models are estimated for comparing patterns of covolatility spillovers. The paper provides a new way of analyzing and describing the patterns of covolatility spillovers, which should be useful for the future empirical analysis of estimating and testing covolatility spillover effects.

The effect of universal futures on opening and closing stock market prices

Purpose – In 2001, Euronext-Liffe introduced single security futures contracts for the first time. The purpose of this paper is to examine the impact that these single security futures had on the volatility of the underlying stocks. Design/methodology/approach – The Inclan and Tiao algorithm was used to show that the volatility of underlying securities did not change after universal futures were introduced. Findings – It was found that in the aftermath of the introduction of universal futures the volatility of the underlying securities increases. Increased volatility is not apparent in the control sample. This suggests that single security futures did have some impact on the volatility of the underlying securities. Originality/value – Despite the huge literature that has examined the effects of a futures listing on the volatility of underlying stock returns, little consensus has emerged. This paper adds to the dialogue by focusing on the effects of a single security futures contract rather than concentrating on the effects of index futures contracts.

On Option Price Information Content and Extraction of Implied Probability Distributions

In this study we used market settlement prices of European call options on stock index futures to extract implied probability distribution function (PDF). The method used produces a PDF of returns of an underlying asset at expiration date from implied volatility smile. With this method, the assumption of lognormal distribution (Black-Scholes model) is tested. The market view of the asset price dynamics can then be used for various purposes (hedging, speculation). We used the so called smoothing approach for implied PDF extraction presented by Shimko (1993). In our analysis we obtained implied volatility smiles from index futures markets (S&P 500 and DAX indices) and standardized them. The method introduced by Breeden and Litzenberger (1978) was then used on PDF extraction. The results show significant deviations from the assumption of lognormal returns for S&P500 options while DAX options mostly fit the lognormal distribution. A deviant subjective view of PDF can be used to form a strategy as discussed in the last section.

Liukuviin keskiarvoihin perustuvien kaupankäyntistrategioiden suoriutuminen Suomen osakemarkkinoilla

Tämän tutkimuksen tarkoituksena on selvittää, pystyykö teknistä analyysiä hyväksikäyttävä sijoittaja saamaan markkinatuottoa parempaa tuottoa Suomen osakemarkkinoilla. Tutkielman aineisto koostuu 24:stä vaihdetuimmasta osakkeesta Helsingin pörssissä. Nämä osakkeet muodostavat OMX25 –indeksin lukuun ottamatta yhtä osaketta, jota ei oltu vielä noteerattu tarkasteluperiodin alussa. Teknisen analyysin menetelminä käytetään neljää eripituista liukuvaa keskiarvoa (5, 20, 50 ja 100). Näistä muodostetaan liukuvien keskiarvojen kaksinkertaiset leikkausmenetelmät, joiden avulla saadaan osto- ja myyntisignaaleja kullekin osakkeelle. Tutkielman vertailukohteena käytetään perinteisen rahoitusteorian suosimaa osta ja pidä -strategiaa. Empiiristen testien tarkastelujakso on 1.1.2006 – 30.9.2010. Tutkielmassa havaittiin, että teknistä analyysiä hyväksikäyttäen voi saada markkinoita parempaa tuottoa, vaikka kaikki tulokset eivät olleet tilastollisesti merkittäviä. Tutkimuksessa ei otettu huomioon useista kaupoista syntyviä transaktiokustannuksia, veroja eikä korkotuottoa, jonka sijoittaja saisi pitäessään varoja esimerkiksi pankkitilillä ennen seuraavaa kauppaa. Erityisen huomionarvoista tässä tutkimuksessa oli se, että tekninen analyysi antoi sijoittajalle erittäin hyvän suojan finanssikriisin aikaiselta kurssilaskulta. Se antoi sijoittajalle selvän myyntisignaalin myydä osakkeet, ennen kuin kurssit alkoivat laskea rajusti.

Evaluating interval forecasts of high-frequency financial data

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated.

Normal backwardation é normal no mercado futuro brasileiro?

A pesquisa testa a existência, no mercado futuro brasileiro, do fenômeno que Keynes denominou de normal backwardation, isto é, a hipótese de que os preços futuros não são estimadores não viesados (unbiased estimators) do preço à vista esperado para o futuro. Quatro contratos futuros negociados na BM&F Bolsa de Mercadorias e Futuros foram estudados, a saber, futuro de Ibovespa, futuro de dólar comercial, futuro de boi gordo e futuro de café arábica, cobrindo o período de julho de 1994 a setembro de 1997. Cada contrato futuro citado foi submetido a quatro testes, sugeridos pelas implicações da hipótese de Keynes. Nossos resultados indicam que normal backwardation não é normal no mercado futuro brasileiro, repetindo as conclusões de vários estudos internacionais.

Financial markets--preliminary observations on the October 1987 crash : report to congressional requesters /

"GAO/GGD-88-38."

Iowa Leading Indicators Index Report, July 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, August 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, September 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, October 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, November 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, December 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, January 2007

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, February 2007

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.