nep-fmk New Economics Papers
on Financial Markets
Issue of 2009‒11‒27
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Disasters implied by equity index options By Backus, David; Chernov, Mikhail; Martin, Ian
  2. The Financial Crisis and Money Markets in Emerging Asia By Rigg, Robert; Schou-Zibell, Lotte
  3. Statistical Regularities of Equity Market Activity By Fengzhong Wang; Kazuko Yamasaki; Shlomo Havlin; H. Eugene Stanley
  4. Modelling Australian Stock Market Volatility: A Multivariate GARCH Approach By Valadkhani, Abbas; O'Brien, Martin; Karunanayake, Indika
  5. "Dynamic Conditional Correlations in International Stock, Bond and Foreign Exchange Markets: Emerging Markets Evidence" By Abdul Hakim; Michael McAleer
  6. "VaR Forecasts and Dynamic Conditional Correlations for Spot and Futures Returns on Stocks and Bonds" By Abdul Hakim; Michael McAleer
  7. Credit derivatives: instruments of hedging and factors of instability. The example of ?Credit Default Swaps? on French reference entities By Nathalie Rey
  8. "Modelling Long Memory Volatility in Agricultural Commodity Futures Returns" By Roengchai Tansuchat; Chia-Lin Chang; Michael McAleer
  9. "Pricing Average Options on Commodities" By Kenichiro Shiraya; Akihiko Takahashi
  10. The Superiority of Time-Varying Hedge Ratios in Turkish Futures By Onur Olgun; Ý. Hakan Yetkiner

  1. By: Backus, David; Chernov, Mikhail; Martin, Ian
    Abstract: We use prices of equity index options to quantify the impact of extreme events on asset returns. We define extreme events as departures from normality of the log of the pricing kernel and summarize their impact with high-order cumulants: skewness, kurtosis, and so on. We show that high-order cumulants are quantitatively important in both representative-agent models with disasters and in a statistical pricing model estimated from equity index options. Option prices thus provide independent confirmation of the impact of extreme events on asset returns, but they imply a more modest distribution of them.
    Keywords: cumulants; entropy; equity premium; implied volatility; pricing kernel; risk-neutral probabilities
    JEL: E44 G12
    Date: 2009–08
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:7416&r=fmk
  2. By: Rigg, Robert; Schou-Zibell, Lotte (Asian Development Bank)
    Abstract: Asian money markets entered the financial crisis in better shape than markets in other regions due to a substantial build-up of savings and liquidity in their banking systems, as well as a greater domestic focus in most of the region’s markets. However, despite the higher liquidity and lower levels of global integration, the effects of the crisis in Asia were severe and followed a similar path observed in international markets. The further development of money markets, particularly in less developed economies, will require policies and initiatives that add liquidity and depth to attract broader participation from both domestic and international investors—including regional cooperation, a robust regulatory architecture, and foreign competition to expedite the development of less developed money markets. Risk management and liquidity assumptions also need to be enhanced to establish buffers that will withstand more severe and prolonged external shocks and disruptions to external financing.
    Keywords: Money market; money market participants; components of money markets; financial crisis
    JEL: F30 G00 G20 O53
    Date: 2009–11–01
    URL: http://d.repec.org/n?u=RePEc:ris:adbrei:0038&r=fmk
  3. By: Fengzhong Wang; Kazuko Yamasaki; Shlomo Havlin; H. Eugene Stanley
    Abstract: Equity activity is an essential topic for financial market studies. To explore its statistical regularities, we comprehensively examine the trading value, a measure of the equity activity, of the 3314 most-traded stocks in the U.S. equity market and find that (i) the trading values follow a log-normal distribution; (ii) the standard deviation of the growth rate of the trading value obeys a power-law with the initial trading value, and the power-law exponent beta=0.14. Remarkably, both features hold for a wide range of sampling intervals, from 5 minutes to 20 trading days. Further, we show that all the 3314 stocks have long-term correlations, and their Hurst exponents H follow a normal distribution. Furthermore, we find that the Hurst exponent depends on the size of the company. We also show that the relation between the scaling in the growth rate and the long-term correlation is consistent with beta=1-H, similar to that found recently on human interaction activity by Rybski and collaborators.
    Date: 2009–11
    URL: http://d.repec.org/n?u=RePEc:arx:papers:0911.4258&r=fmk
  4. By: Valadkhani, Abbas (University of Wollongong); O'Brien, Martin (University of Wollongong); Karunanayake, Indika
    Abstract: This paper uses a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model to provide an insight into the nature of interaction between stock market returns of four countries, namely, Australia, Singapore, the UK, and the US. Using weekly data spanning from January 1992 to December 2008 the results indicate that all markets (particularly Australia and Singapore) display significant positive mean-spillovers from the US stock market returns but not vice versa. We also found strong evidence for both own and cross ARCH and GARCH effects among all four markets, indicating the existence of significant volatility and cross volatility spillovers across all four markets. Given a high degree of common time-varying co-volatility among these four countries, investors will be highly unlikely to benefit a reduction of risk if they diversify their financial portfolio with stocks from these four countries only
    Keywords: Multivariate GARCH; Stock returns; Volatility, Australia
    JEL: C32 G11 G15
    Date: 2009
    URL: http://d.repec.org/n?u=RePEc:uow:depec1:wp09-11&r=fmk
  5. By: Abdul Hakim (Faculty of Economics, Indonesian Islamic University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)
    Abstract: The paper models the dynamic conditional correlations in emerging stock, bond and foreign exchange markets using the DCC model of Engle (2002) and the GARCC model of McAleer et al. (2008). The highly restrictive DCC model suggests that the conditional correlations of the overall returns are constant. In contrast, the GARCC model finds that the conditional correlations between bond-bond markets and between stock-stock markets are relatively constant across developed-emerging markets, while those between emerging-emerging markets are dynamic. The conditional correlations between stock-bond markets across developed-emerging markets are also more dynamic as compared with those between emerging-emerging markets.
    Date: 2009–10
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf677&r=fmk
  6. By: Abdul Hakim (Faculty of Economics, Indonesian Islamic University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)
    Abstract: The paper investigates the interdependence and conditional correlations between futures contracts and their underlying assets, both for stock and bond markets, and the impact of the interdependence and conditional correlations on VaR forecasts. The paper finds evidence of volatility spillovers from spot (futures) to futures (spot) markets, and time-varying conditional correlations between futures and their underlying assets. It also finds evidence that the DCC model of Engle (2002) provides slightly better VaR forecasts as compared with the CCC model of Bollerslev (1990) and the BEKK model of Engle and Kroner (1995).
    Date: 2009–10
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf676&r=fmk
  7. By: Nathalie Rey (CEPN)
    Abstract: Through a long-period analysis of the inter-temporal relations between the French markets for credit default swaps (CDS), shares and bonds between 2001 and 2008, this article shows how a financial innovation like CDS could heighten financial instability. After describing the operating principles of credit derivatives in general and CDS in particular, we construct two difference VAR models on the series: the share return rates, the variation in bond spreads and the variation in CDS spreads for thirteen French companies, with the aim of bringing to light the relations between these three markets. According to these models, there is indeed an interdependence between the French share, CDS and bond markets, with a strong influence of the share market on the other two. This interdependence increases during periods of tension on the markets (2001-2002, and since the summer of 2007).
    Date: 2009–11
    URL: http://d.repec.org/n?u=RePEc:arx:papers:0911.4039&r=fmk
  8. By: Roengchai Tansuchat (Faculty of Economics, Maejo University); Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)
    Abstract: This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGACH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.
    Date: 2009–10
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf680&r=fmk
  9. By: Kenichiro Shiraya (Mizuho-DL Financial Technology Co.,Ltd.); Akihiko Takahashi (Faculty of Economics, University of Tokyo)
    Abstract: This paper proposes a new approximation formula for average options on commodities under stochastic volatility environment. In particular, it derives a formula under two stochastic volatility models such as Heston and ă-SABR models including the SABR model as a special case by using an asymptotic expansion method. To our knowledge, this paper is the first one that shows an analytic (approximation) formula under stochastic volatility models for valuation of average options structured for commodity contracts. Then, it confirms its sufficient accuracy through numerical examples. It also implements calibration to the WTI futures option market that is one of the most liquid commodity markets. Using the parameters obtained by calibration, it compares model-based prices with those of traded average options in NYMEX.
    Date: 2009–10
    URL: http://d.repec.org/n?u=RePEc:tky:fseres:2009cf681&r=fmk
  10. By: Onur Olgun (Department of International Trade and Finance, Izmir University of Economics); Ý. Hakan Yetkiner (Department of Economics, Izmir University of Economics)
    Abstract: This paper aims to compare the effectiveness of constant hedge ratio estimates (obtained through OLS and VECM methods) and time-varying hedge ratio estimates (obtained via M-GARCH method) for future contracts of ISE-30 index of TurkDEX. We use portfolio variance reduction as the measure of hedging effectiveness. We find that time-varying hedge ratios outperform the constant ratios for both in-sample and out-of-sample datasets and provide the minimum variance values.
    Keywords: Futures Pricing, Hedging, MGARCH, Hedging Effectiveness
    JEL: G13
    Date: 2009–11
    URL: http://d.repec.org/n?u=RePEc:izm:wpaper:0907&r=fmk

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