Inflation and inflation uncertainty in China

The study examines the relationship between inflation and inflation uncertainty in China using Generalized Autoregressive Conditional Heteroscedasticity model. Particularly, this link is investigated in China’s urban and rural sectors, motivated by the substantial urban–rural divide. The results provide strong statistical supportive evidence that higher inflation raises inflation uncertainty. On the other hand, evidence on the effect of inflation uncertainty on inflation is mixed depending on the sample periods and areas examined. The understanding of inflation-uncertainty nexus in China could provide implications to policymakers in the adoption of monetary policies.     

Pricing executive stock options with averaging features under the Heston–Nandi GARCH model

In this paper, we focus on the pricing issue of four types of executive stock options (ESOs) in the Heston–Nandi generalized autoregressive conditional heteroskedasticity option pricing model. Based on the derived benchmark strike prices in the proposed framework, we obtain the closed-form pricing formulae for four types of ESOs. In the numerical part, we investigate the sensitivity and cost efficiency of ESOs and suggest that systematic risk (stock β) and the fraction of wealth invested in restricted stock could impede the cost efficiency of ESOs.     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.     

Pairs trading strategies in a cointegration framework: back-tested on CFD and optimized by profit factor

Statistical arbitrage is based on pairs trading of mean-reverting returns. We used cointegration approach and ECM-DCC-GARCH to construct 98 pairs of 152 stocks of 3 currencies. Stocks trading is done by Contract for Difference (CFD), a financial derivative product which facilitates short selling and provides a leverage up to 25 times. To measure the performance of a leveraged strategy, we introduced the profit factor which is the annualized return rate per unit risk. And the historical risk is measured by maximum drawdown. We compared three main strategies: percentage, standard deviation of cointegration long-term residuals and Bollinger Bands (dynamic standard deviation), with and without double confirmation of short-term standard deviation modelled by ECM-DCC-GARCH. Each of the three main strategies is optimized by two optimizers: absolute profit and profit factor. The optimization period goes from 2012–01-01 to 2014–12-31, and validation period is from 2015–01-01 to 2016–06-01. Our results showed that the USD Bollinger Bands strategy without double confirmation and optimized by profit factor, outperformed other strategies and provided the highest annualized return rate per unit risk; 32% of our sample pairs ended up in loss, and 94% of which are explained by a cointegration break during the testing period.     

Variance swaps valuation under non-affine GARCH models and their diffusion limits

In this article, we investigate the pricing and convergence of general non-affine non-Gaussian GARCH-based discretely sampled variance swaps. Explicit solutions for fair strike prices under two different sampling schemes are derived using the extended Girsanov principle as the pricing kernel candidate. Following standard assumptions on time-varying GARCH parameters, we show that these quantities converge respectively to fair strikes of discretely and continuously sampled variance swaps that are constructed based on the weak diffusion limit of the underlying GARCH model. An empirical study which relies on a joint estimation using both historical returns and VIX data indicates that an asymmetric heavier tailed distribution is more appropriate for modelling the GARCH innovations. Finally, we provide several numerical exercises to support our theoretical convergence results in which we further investigate the effect of the quadratic variation approximation for the realized variance, as well as the impact of discrete versus continuous-time modelling of asset returns.     

Valuation of VIX and target volatility options with affine GARCH models

In this paper we propose semiclosed-form solutions, subject to an inversion of the Fourier transform, for the price of VIX options and target volatility options under affine GARCH models based on Gaussian and Inverse Gaussian distributions. We illustrate the advantage of the proposed analytic expressions by comparing them with those obtained from benchmark Monte–Carlo simulations. The empirical performance of the two affine GARCH models is tested using different calibration exercises based on historical returns and market quotes on VIX and SPX options.     

Nonlinear causality testing with stepwise multivariate filtering: Evidence from stock and currency markets

We examine the spillovers of the US subprime crisis to Asian and European economies and in particular to what extent currency and stock markets have been affected by the crisis. Linear and nonlinear dependencies are detected after pairwise and system-wise causality analysis. A new stepwise multivariate filtering approach is implemented after controlling for conditional heteroskedasticity in the raw data and in VAR/VECM residuals using multivariate GARCH models. Significant nonlinear causal linkages persisted even after the application of GARCH-BEKK, CCC-GARCH and DCC-GARCH modelling. This indicates that volatility effects might partly induce nonlinear causality. Perhaps new short-term asset-pricing models could be developed to explain this stylized fact. These results might also have important implications for hedging, trading strategies and financial market regulation.     

The information content of implied volatility in developed versus developing FX markets

This article predicts the daily movement of monthly foreign exchange (FX) rate volatility using a linear combination of a time-series model and implied volatilities from options. The focus is on analysing the FX volatilities in three developing economies (the Brazilian real (BRL), the Indian rupee (INR) and the Russian ruble (RUB)) against the US dollar (USD). The empirical exercise utilizes two time-series models, mixed data sampling (MIDAS) and GARCH. The analysis indicates that for both developed and developing economies the predictive power of MIDAS and that of GARCH is comparable. Further on in this article, we will ascertain whether the relationship between realized and implied volatility is fundamentally different in the case of developing economies from that among developed economies. Thus, we compare the pairs USD/BRL, USD/INR and USD/RUB against EURO/USD and USD/Japanese yen to determine the information content and predictive power of implied volatilities. Plots of the MIDAS coefficients show that the volatility is more persistent in developing economies than in developed economies.     

A nesting framework for Markov-switching GARCH modelling with an application to the German stock market

In this paper, we establish a generalized two-regime Markov-switching GARCH model which enables us to specify complex (symmetric and asymmetric) GARCH equations that may differ considerably in their functional forms across the two Markov regimes. We show how previously proposed collapsing procedures for the Markov-switching GARCH model can be extended to estimate our general specification by means of classical maximum-likelihood methods. We estimate several variants of the generalized Markov-switching GARCH model using daily excess returns of the German stock market index DAX sampled during the last decade. Our empirical study has two major findings. First, our generalized model outperforms all nested specifications in terms of (a) statistical fit (when model selection is based on likelihood ratio tests) and (b) out-of-sample volatility forecasting performance. Second, we find significant Markov-switching structures in German stock market data, with substantially differing volatility equations across the regimes.     

Volatility Relationship between Crude Oil and Petroleum Products

This paper utilizes calculated historical volatility and GARCH models to compare the historical price volatility behavior of crude oil, motor gasoline and heating oil in U.S. markets since 1990. We incorporate a shift variable in the GARCH/TARCH models to capture the response of price volatility to a change in OPEC’s pricing behavior. This study has three major conclusions. First, there was an increase in volatility as a result of a structural shift to higher crude oil prices after April 1999. Second, volatility shocks from current news are not important since GARCH effects dominate ARCH effects in the variance equation. Third, persistence of volatility in all commodity markets is quite transitory, with half-lives normally being a few weeks.