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

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.     

Inflation targeting in Latin America: Empirical analysis using GARCH models

This paper studies inflation dynamics in eight Latin American countries, some of which have adopted formal inflation targets (IT) as their monetary policy frameworks. We analyze the possible benefits associated with IT, not only in terms of inflation level and volatility, but also regarding other nonlinear characteristics of these series, such as volatility persistence or the fulfillment of the Friedman hypothesis. To describe inflation dynamics we use an unobserved components model, where each component can follow a GARCH type process. Once we estimate the model, the main findings of the empirical exercise confirm the favorable performance of IT.     

Structural models, information and inherited restrictions

The derived structural estimates of the system βY = γZ + δU impose identifying restrictions on the reduced form estimates ex post. Some or all of the derived structural estimates are presented as evidence of the model's efficacy. In fact, the reduced form inherits a great deal of information from the structure's restrictions and hypothesized sign patterns, limiting the allowable signs for the reduced form. A method for measuring a structural model's statistical information content is proposed. Further, the paper develops a method for enumerating the allowable reduced form outcomes which can be used to falsify the proposed model independently of significant coefficients found for the structural relations.     

The FMLS-based CUSUM statistic for testing the null of smooth time-varying cointegration in the presence of a structural break

In this paper we extend the FMLS-based CUSUM cointegration test (Xiao and Phillips, 2002) for testing the smooth time-varying cointegration null hypothesis. For this purpose we use Chebyshev time polynomials to specify time-varying coefficients under the null. We derive the limiting distribution of the statistic, which is pivotal with the order of the Chebyshev time polynomials, and we provide the critical values to conduct the proposed test.     

Modeling hedge fund exposure to risk factors

This paper examines the adjustment dynamics of hedge fund returns and studies their exposure to risk factors in a nonlinear framework for several types of strategies over the last two decades. Nonlinearity is justified by distortions due to the use of short selling, leverage, derivatives and illiquid assets for hedge fund strategies. Among nonlinear models, switching regime (STR) models are applied to reproduce the dynamics of hedge fund returns. This nonlinear multivariate modeling has the advantage of capturing the time-varying exposure of hedge fund strategies to risk factors, and of specifying the asymmetric relationship between hedge fund returns and risk. The findings are interesting and provide several contributions to the hedge fund literature. First, we show that the dynamics of hedge fund returns exhibit significant asymmetry and nonlinearity, indicating that they evolve and vary asymmetrically in accordance with stages in financial cycles. Second, hedge fund exposure to risk factors also varies over time, depending on the strategy and the regime. Finally, our modeling captures the most important changes in hedge fund exposure to risk factors induced by the recent global financial crisis (2008–2009).     

Intraday return and volatility relationships between the Ibex 35 spot and futures markets

This paper analyses the intraday lead-lag relationships between returns and volatilities in the Ibex 35 spot and futures markets. Using hourly data, we jointly analyze the interactions between markets, estimating a bivariate error correction model with GARCH perturbations which captures stochastically the presence of an intraday U-shaped curve for both spot and futures market volatility. Our findings show a bidirectional causal relationship between market volatilities, with a positive feedback. This two-way transmission of volatility is consistent with market prices evolving according to a long-run equilibrium relationship, and shocks affecting both markets in the same direction. Our empirical results also support a unidirectional cross interaction from futures to spot market returns. This pattern suggests that the futures market leads the spot market in order to incorporate the arrival of new information.     

The impact of information signals on market prices when agents have non-linear trading rules

Several methods have been developed for filtering seasonal influences and extreme returns in financial and economic time series. The theoretical support for these approaches is rather questionable since it focuses on the effects of shocks on prices and not on their sources. Removing such effects modifies the true generating system of market dynamics because of the non-proportional character of non-linearity. Thus, taking into account that the underlying process of economic time series is highly non-linear we cannot be certain a priori what the impact of new information will be on the dynamic structure of a system. The main contribution of this paper is to demonstrate using the methodology of simulations the eventual distortions in time series data arising from the arrival of news when agents follow non-linear trading strategies. We argue that if news can really modify the dynamical behaviour of a system, then the methodology of filtering exogenous distortions needs to be re-examined.     

Power monotonicity in detecting volatility levels change

We show that the CUSUM and LM tests for structural change in the volatility process enjoy monotonic power. The framework is general including many recently proposed non-stationary GARCH-type models. The result is in contrast to the well-known issue of non-monotonic power for the CUSUM-based tests for changing mean. Simulations and an empirical example provide further support.     

Stock exchange mergers and return co-movement: A flexible dynamic component correlations model

The creation of a common cross-border stock trading platform is found, by use of a Flexible Dynamic Component Correlations (FDCC) model, to have increased long-run trends in conditional correlations between foreign and domestic stock market returns.     

Realized volatility transmission: The role of jumps and leverage effects

This paper is the first to employ a multivariate extension of the LHAR–CJ model for realized volatility of Corsi and Renó (2012) considering continuous and jump volatility components and leverage effects. The model is applied to financial (S&P 500), commodity (WTI crude oil) and forex (US$/EUR) intraday futures data and allows new insights in the transmission mechanisms among these markets. Besides significant leverage effects, we find that the jump components of all considered assets do not contain incremental information for the one-step ahead realized volatility. The volatility of S&P 500 and US$/EUR exchange rate futures exhibits significant spillovers to the realized volatility of WTI. Moreover, decreasing equity prices appear to increase volatility in other markets, while strengthening of the US$ seems to calm down the crude oil market.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

Dynamic modeling under linear-exponential loss

We develop a methodology of parametric modeling of time series dynamics when the underlying loss function is linear-exponential (Linex). We propose to directly model the dynamics of the conditional expectation that determines the optimal predictor. The procedure hinges on the exponential quasi-maximum likelihood interpretation of the Linex loss and nicely fits the multiplicative error modeling framework. Many conclusions relating to estimation, inference and forecasting follow from results already available in the econometric literature. The methodology is illustrated using data on United States GNP growth and Treasury bill returns.     

Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models

This paper studies the nonlinear adjustment between industrial production and carbon prices – coined as ‘the carbon-macroeconomy relationship’ – in the EU 27. We model carbon price returns and industrial production as nonlinear and state-dependent, with dynamics depending on the sign and magnitude of past realization of returns and the growth of industrial production. Our findings show that (i) macroeconomic activity is likely to affect carbon prices with a lag, due to the specific institutional constraints of this environmental market; (ii) the joint dynamics of industrial production and carbon prices seem adequately captured by two-regime threshold vector error-correction and two-regime Markov-switching VAR models compared to linear models as main competitors. The regime-switching models proposed are profoundly checked for their economic content and statistical congruency, and are found to provide a sound statistical framework for a comprehensive analysis of the carbon-macroeconomy relationship.     

Official central bank interventions and exchange rate volatility: Evidence from a regime-switching analysis

In this paper, we investigate the effect of central bank interventions on the weekly returns and volatility of the DEM/USD and YEN/USD exchange rate returns. In contrast with previous analyses, we allow for regime-dependent specifications and investigate whether official interventions can explain the observed volatility regime switches. It is found that, depending on the prevailing volatility level, coordinated central bank interventions can lead to either a stabilizing or a destabilizing effect. Our results lead us to challenge the usual view that such interventions always imply increases in volatility.     

Monetary environments and stock returns revisited: A quantile regression approach

We investigate the impact of monetary conditions on stock market returns at different points on the return distributions. Our results reveal no association between stock returns and monetary environments at the lower quantiles. At the upper quantiles, however, we find that expansive monetary conditions lead to significantly larger stock returns. The relationship between returns and monetary conditions at the upper quantiles is also found to be asymmetric, exhibiting a monotonic increase in responsiveness at successive quantiles.     

Can we reject linearity in an HAR-RV model for the S&P 500? Insights from a nonparametric HAR-RV

This article tests the linearity assumption underlying the popular heterogeneous autoregressive model for realized volatility (HAR-RV). We implement a consistent model specification test that is robust to both distributional and model misspecification. We find that, using a nonparametric HAR-RV (NPHAR-RV), we are unable to reject the null of linearity.     

GARCH with omitted persistent covariate

This paper analyzes the effect of omitting a persistent covariate in the GARCH-X model. In particular, we show that if the relevant persistent covariate is omitted and the usual GARCH(1,1) model is fitted, the model will be estimated approximately as an IGARCH model. This may well explain the ubiquitous evidence of the IGARCH in empirical volatility analysis.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries

While the relationship between oil prices and stock markets is of great interest to economists, previous studies do not differentiate oil-exporting countries from oil-importing countries when they investigate the effects of oil price shocks on stock market returns. In this paper, we address this limitation using a structural VAR analysis. Our main findings can be summarized as follows: First, the magnitude, duration, and even direction of response by stock market in a country to oil price shocks highly depend on whether the country is a net importer or exporter in the world oil market, and whether changes in oil price are driven by supply or aggregate demand. Second, the relative contribution of each type of oil price shocks depends on the level of importance of oil to national economy, as well as the net position in oil market and the driving forces of oil price changes. Third, the effects of aggregate demand uncertainty on stock markets in oil-exporting countries are much stronger and more persistent than in oil-importing countries. Finally, positive aggregate and precautionary demand shocks are shown to result in a higher degree of co-movement among the stock markets in oil-exporting countries, but not among those in oil-importing countries.