27 research outputs found

    Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash

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    Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.volatilities and correlations, weekly returns, multivariate t, financial interdependence, VaR diagnostics, 2008 stock market crash

    Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution

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    This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and equity index futures. The results strongly reject the normal-DCC model in favour of a t-DCC specification. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results suggest a general trend towards a lower level of return volatility, accompanied by a rising trend in conditional cross correlations in most markets; possibly reflecting the advent of euro in 1999 and increased interdependence of financial markets.volatilities and correlations, futures market, multivariate t, financial interdependence, VaR diagnostics

    Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash

    Get PDF
    Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis

    Conditional volatility and correlations of weekly returns and the VaR analysis of 2008 stock market crash

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    Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis

    Conditional volatility and correlations of weekly returns and the VaR analysis of 2008 stock market crash

    No full text
    Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.Volatilities and correlations Weekly returns Multivariate t Financial interdependence VaR diagnostics 2008 stock market crash

    The Statistical Distribution of Short-Term Libor Rates Under Two Monetary Regimes

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    The paper presents a statistical analysis of sterling libor interest rates in two monetary regimes: free-floating of sterling prior to ERM-entry, and the recent ERM regime. It is found that short-term libor rates follow a random walk with time-varying volatility and with interest rate changes drawn from a distribution with fat tails, (sic). The nature of interest rate changes is sensitive to monetary regime: interest rate changes in the pre-ERM regime are drawn from a distribution with much faster tails than for the ERM regime. These statistical characterisations of libor rates are inconsistent with existing models for pricing interest rate and bond options, which assume either that interest rates follow a random walk with constant volatility, or that interest rates are mean-reverting.

    An assessment of the relative importance of real interest rates, inflation and term premia in determining the prices of real and nominal UK bonds

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    This paper uses a dynamic accounting identity developed by Campbell to decompose movements in bond prices into elements due to changes in real interest rates, expected term premia and expected inflation. This decomposition is applied to UK short and long-maturity nominal bonds and index-linked bonds using data between 1983 and 1993. The main findings are that changes in expected inflation are by far the most important determinant of bond price movements. So much so that even for index-linked bonds changes in expected inflation (which have an effect due to the eight month indexation lag) are a more important factor than changes in real interest rates (which contribute less than 3% to the variance of index-linked bond prices). The paper also finds that changes in expected term premia are an important determinant of changes in both nominal and index-linked bond prices. However, the term premia appears to be a common factor which has little influence on the relative price of the two types of bond (ie break-even inflation rates). This suggests that changes in the relative price of the two type of bonds offer a reliable measure of changes in market expectations of inflation with about 95% of the variance of relative yields being due to revisions to expected inflation.
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