Ioannis Papantonis

In this paper, we effectively extend the Realized-EGARCH (R-EGARCH) framework by allowing the conditional variance process to incorporate exogenous variates related to different observable features of Realized Variance (RV). The choice of these features is well motivated by recent studies on the Heterogeneous Autoregressive (HAR) class of models. We examine several specifications nested within our augmented R-EGARCH representation, and we find that they perform significantly better than the standard R-EGARCH model. These specifications incorporate realized semi-variances, heterogeneous long-memory effects of RV, and jump variation. We also show that the performance of our framework further improves if we allow for skewness and excess kurtosis for asset return innovations, instead of assuming normality. This can better filter the true distribution of the return innovations, and thus can more accurately estimate their effects on the variance process. This is also supported by a Monte Carlo simulation exercise executed in the paper.

This paper extends the Realized-GARCH framework, by allowing the conditional variance equation to incorporate exogenous variables related to intra-day realized measures. The choice of these measures is motivated by the so-called heterogeneous auto-regressive (HAR) class of models. Our augmented model is found to outperform both the Realized-GARCH and the various HAR models in terms of in-sample fitting and out-of-sample forecasting accuracy. The new model specification is examined under alternative parametric density assumptions for the return innovations. Non-normality seems to be very important for filtering the return innovations to which variance responds and helps significantly upon the prediction performance of the suggested model.

In this paper we explore important implications of capturing volatility risk premium (VRP) within a parametric GARCH setting. We study the transmission mechanism of shocks from returns to risk-neutral volatility by providing an examination of the news-impact curves and impulse–response functions of risk-neutral volatility, in order to better understand how option prices respond to return innovations. We report a value of −3% for the magnitude of the average VRP and we recover the empirical densities under physical and risk-neutral measures. Allowing for VRP is crucial for adding flexibility to the shape of the two distributions. In our estimation procedure, we adopt a MLE approach that incorporates both physical return and risk-neutral VIX dynamics. By introducing volatility - instead of variance - innovations in the joint likelihood function and by allowing for contemporaneous correlation between innovations in returns and the VIX we show that we may critically reduce the bias and improve the efficiency of the joint maximum likelihood estimator, especially for the parameters of the volatility process. Modeling returns and the VIX as a bi-variate normal permits identification of a contemporaneous correlation coefficient of approximately −30% between returns and risk-neutral volatility.

Purpose

The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations.

Design/methodology/approach

First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency.

Findings

The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected.

Originality/value

The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

Purpose

This paper aims to enhance a co-skew-based risk measurement methodology initially introduced in Polimenis, by extending it for the joint estimation of the jump betas for two stocks.

Design/methodology/approach

The authors introduce the possibility of idiosyncratic jumps and analyze the robustness of the estimated sensitivities when two stocks are jointly fit to the same set of latent jump factors. When individual stock skews substantially differ from those of the market, the requirement that the individual skew is exactly matched is placing a strain on the single stock estimation system.

Findings

The authors argue that, once the authors relax this restrictive requirement in an enhanced joint framework, the system calibrates to a more robust solution in terms of uncovering the true magnitude of the latent parameters of the model, at the same time revealing information about the level of idiosyncratic skews in individual stock return distributions.

Research limitations/implications

Allowing for idiosyncratic skews relaxes the demands placed on the estimation system and hence improves its explanatory power by focusing on matching systematic skew that is more informational. Furthermore, allowing for stock-specific jumps that are not related to the market is a realistic assumption. There is now evidence that idiosyncratic risks are priced as well, and this has been a major drawback and criticism in using CAPM to assess risk premia.

Practical implications

Since jumps in stock prices incorporate the most valuable information, then quantifying a stock's exposure to jump events can have important practical implications for financial risk management, portfolio construction and option pricing.

Originality/value

This approach boosts the “signal-to-noise” ratio by utilizing co-skew moments, so that the diffusive component is filtered out through higher-order cumulants. Without making any distributional assumptions, the authors are able not only to capture the asymmetric sensitivity of a stock to latent upward and downward systematic jump risks, but also to uncover the magnitude of idiosyncratic stock skewness. Since cumulants in a Levy process evolve linearly in time, this approach is horizon independent and hence can be deployed at all frequencies.