VIX Futures Calendar Spreads (with Lars Norden)
A VIX futures calendar spread involves buying a futures contract maturing in one month and selling another one maturing in a different month. VIX futures calendar spreads represent a daily turnover above 500 million dollars, or roughly 20% of the total VIX futures trading volume. A calendar spread trade is a bet on the change in the slope of the volatility term structure. We find that speculation, rather than information about changes in the slope of the volatility term structure, is driving calendar spread trades. On average, a calendar spread costs a little less than $100 (about 15 basis points). If settled at the end
of the trading day, 43% of the calendar spreads are profitable.
Determinants of Time Varying Co-movements among International Stock Markets during Crisis and Tranquil Periods (with Mobarek, A., Bobarek, B., Muradoglu,G.,)
In this paper, we use the DCC MIDAS approach to assess the validity of the wake-up call hypothesis for developed and emerging markets during the global financial crisis (GFC). We use this approach to decompose the total correlations into short- (daily) and long-run (quarterly) correlations for the period from 1999 to 2011. We then examine the transmission mechanisms by regressing the quarterly economic, financial, and behavioural variables on the quarterly DCC-MIDAS correlations. We find that country specific factors are crisis contingent transmission mechanisms for the co-movements of emerging country pairs and mixed pairs of advanced and emerging countries during the global financial crisis. However, we do not observe wake-up calls in the transmission of the crisis among advanced country pairs. The classification of the transmission mechanisms for crisis and non-crisis periods with the different country pairs has important implications for crisis management as well as for portfolio investment strategies. Thus, our findings contribute to the discussion on the role and effectiveness of the international financial architecture.
Macro-Finance Determinants of the Long-Run Stock-Bond Correlations: The DCC-MIDAS Specification
(with Hossein Asgharain, Charlotte Christiansen)
We investigate long-run stock-bond correlation using a model that combines the dynamic conditional correlation model with the mixed-data sampling approach and allows long-run correlation to be affected by macro-finance factors (historical and forecasts). We use macro-finance factors related to inflation and interest rates, illiquidity, state of the economy, and market uncertainty. Macro-finance factors, particularly their forecasts, are good at forecasting long-run stock-bond correlation. Supporting the flight-to-quality phenomenon, long-run correlation tends to be small and negative when the economy is weak.
Effective of Macroeconomic Uncertainty on the stock and bond markets (with Hossein Asgharain, Charlotte Christiansen)
In this paper we show that the long-run stock and bond volatility and the long-run stock-bond correlation depend on macroeconomic uncertainty. We use the mixed data sampling (MIDAS) econometric approach. The findings are in accordance with the flight-to-quality phenomenon when macroeconomic uncertainty is high.
EMU Equity Markets' Return Varianceand Spill Over Effects from Short-term Interest Rates
This paper examines the spillover effects from the short-term interest-rates market to equity markets within the Euro area. The empirical study is carried out by estimating an extended Markov-switching Glosten-Jagannathan-Runkle (GJR)-in-mean model with a Bayesian-based Markov Chain Monte Carlo methodology. The results indicate that stock markets in the Euro area display two significant regimes with distinct characteristics. Within a bear-market regime, stock returns have a negative relationship with volatility, and the volatility process responds asymmetrically to negative shocks to equity returns. The other regime appears to be a bull-market regime, within which the returns have a positive relationship with volatility, and volatility is lower and more persistent. We find also that there is a significant impact from fluctuations in short-term interest rates on the conditional variance and conditional returns in the Economic and Monetary Union countries. This impact is asymmetrical and appears to be stronger in bear markets and when interest rates change upward.
Importance of the Macroeconomic Variables for Volatility Prediction: A GARCH-MIDAS Approach
(with Hossein Asgharian, Ai Jun Hou, and Javed Farrukh)
This paper applies the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long- term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle
A NonParametrical GARCH Model of Crude Oil Price Return Volatility (with Sandy Suardi)
Energy Economics 2012, Vol. 34, Nr. 2, 618-626. Published paper
The use of parametric GARCH models to characterize crude oil price volatility is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility. Focusing on two crude oil markets, Brent and West Texas Intermediate (WTI), we show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. These results are supported by the use of robust loss functions and the Hansen's (2005) superior predictive ability test. The improvement in forecasting accuracy of oil price return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.
Asymmetry Effects in Chinese Stock Markets Volatility: A Generalized Additive Nonparametric Approach
The unique characteristics of the Chinese stock markets make it difficult to assume a particular distribution for innovations in returns and the specification form of the volatility process when modelling return volatility with the parametric GARCH family models. This paper therefore applies a generalized additive nonparametric smoothing technique to examine the volatility of the Chinese stock markets. The empirical results indicate that an asymmetric effect of negative news exists in the Chinese stock markets. Furthermore, compared with other parametric models, the generalized additive nonparametric model demonstrates a better performance for return volatility forecasts, particularly for the out-of-sample forecast. The results from this paper have important implications in risk management, portfolio selection, and hedging strategy.
Modelling and forecasting Short-Term Interest Rate Volatility: A Semi-Parametrical Approach
(with Sandy Suardi)
Journal of Empirical Finance 2011, Vol. 18, Nr. 4, 692-710. Published paper; Working paper version
This paper employs a semiparametric procedure to estimate the diffusion process of short-term interest rates. The Monte Carlo study shows that the semiparametric approach produces more accurate volatility estimates than models that accommodate asymmetry, level effect and serial dependence in the conditional variance. Moreover, the semiparametric approach yields robust volatility estimates even if the short rate drift function and the underlying innovation distribution are misspecified. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semiparametric procedure produces superior in-sample and out-of-sample forecast of short rate changes volatility compared with the widely used single-factor diffusion models. This forecast improvement has implications for pricing interest rate derivatives.