Asset Prices and asset Correlations in Illiquid Markets

We build a new asset pricing framework to study the effects of aggregate illiquidity on asset prices, volatilities and correlations. In our framework the Black-Scholes economy is obtained as the limiting case of perfectly liquid markets. The model is consistent with empirical studies on the effects of illiquidity on asset returns, volatilities and correlations. We present the model, study its qualitative properties and estimate stocks' sensitivities to aggregate liquidity ($\beta$s) using nine years data for 24 randomly sampled stocks traded on the NYSE. These sensitivity parameters ($\beta$s) determine the effect that aggregate illiquidity has on expected returns, volatilities, correlations, CAPM-betas and Sharpe ratios. We find clear capitalization and sector patterns for liquidity $\beta$s.Market Liquidity, Volatilities, Correlations, Asset Pricing, GMM

Bivariate FIGARCH and Fractional Cointegration

We consider the modelling of volatility on closely related markets. Univariate fractional volatility (FIGARCH) models are now standard, as are multivariate GARCH models. In this paper we adopt a combination of the two methodologies. There is as yet little consensus on the methodology for testing for fractional cointegration. The contribution of this paper is to demonstrate the feasibility of estimating and testing cointegrated bivariate FIGARCH models. We apply these methods to volatility on the NYMEX and IPE crude oil markets. We find a common order of fractional integration for the two volatility processes and confirm that they are fractionally cointegrated. An estimated error correction FIGARCH model indicates that the preponderant adjustment is of the IPE towards NYMEX.FIGARCH, Fractional Cointegration, ECM

Long Memory, the 'Taylor Effect' and Intraday Volatility in Commodity Futures Markets

This paper investigates long term dependence in commodity futures markets. Using daily futures returns on cocoa, coffee and sugar, we show that FIGARCH models are able to adequately describe both the long and short run characteristics of commodity market volatility. The paper also considers three measures of risk - squared returns, absolute returns and intraday volatility - and finds that they exhibit the long memory property. Intraday volatility shows the strongest auto correlation structure. Moreover, there is evidence of the so-called "Taylor effect". Key Words: long memory, fractional differentiating; ARFIMA; FIGARCH; squared returns; absolute returns; Taylor effect; intraday volatility

Markov Switching Garch Models of Currency Crises in Southeast Asia

This paper develops a model which is able to forecast exchange rate turmoil. Our starting point relies on the empirical evidence that exchange rate volatility is not constant. In fact, the modeling strategy adopted refers to the vast literature of the GARCH class of models, where the variance process is explicitly modeled. Further empirical evidence shows that it is possible to distinguish between two different regimes: “ordinary” versus “turbulence”. Low exchange rate changes are associated with low volatility (ordinary regime) and high exchange rate devaluations go together with high volatility. This calls for a regime switching approach. In our model we also allow the transition probabilities to vary over time as functions of economic and financial indicators. We find that real effective exchange rate, money supply relative to reserves, stock index returns and bank stock index returns and volatility are the major indicators.Currency crises, Markov Switching Models, Volatility

Trading networks

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139950/1/ectj12090-sup-0001-onlineappendix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139950/2/ectj12090_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139950/3/ectj12090.pd

Markov Switching GARCH Models of Currency Crises in Southeast Asia

revision presented at the annual meeting of the Econometric Society, Washington, D.C., ( 01/2003 : 01/2003

Commodity index trading and hedging costs

Trading by commodity index traders (CITs) has become an important aspect of financial markets over the past 10 years. We develop an equilibrium model of trader behavior that relates uninformed CIT trading to futures prices. The model predicts that CIT trading reduces the cost of hedging. We test the model using a unique non-public dataset which precisely identifies trader positions. We find evidence, consistent with the model, that index traders have become an important supply of price risk insurance.Commodity futures - Mathematical models ; Hedging (Finance) - Mathematical models