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The objective of this paper is to provide a deeper insight into the impact of weekly inventory announcements and especially their associated surprises on crude oil returns and volatility during the period from 27 March 2012 to 2 October 2018. The results can be summarized as follows. First, we find that all inventory surprises exert an inverse impact on the oil price returns. Such impact appears more pronounced during the collapse and post‐collapse periods, than during the pre‐collapse period. Second, we show that the reducing volatility effects of inventory shocks that we establish for the whole period are entirely attributed to those observed in the pre‐collapse period. Third, the different effects obtained for positive and negative shocks support evidence that our split of inventory news by nature matters. Fourth, compared to macroeconomic and unconventional monetary news, the inventory statements appear as the most pertinent news during the event window. Fifth, we find evidence also that the effect of the uncertainty about economic stance, when it is significant, changes the way the oil prices and volatility react to inventory surprises, and hence weakens the effectiveness of the inventory policy. Lastly, we make an original use of the information content of the implied volatility to assess the persistence of the link between the conditional variance and the inventory news. The central finding is that the implied volatility enters with a significant effect on the conditional variance for WTI oil returns and namely with a reduced volatility persistence. Also, a significant effect from inventory surprises namely when we split the announcements into negative and positive news is detected even the implied volatility is introduced exogenously in the GARCH‐type variance specification highlighting the predominance of our main variable. [ABSTRACT FROM AUTHOR]

This paper presents two different approaches of volatility forecasting. One is based on option‐implied volatility (IV), the other involves conducting time series methods using historical volatility. With that purpose, we study eight developed stock markets, offering implied volatility indexes for the 2008 financial crisis. We evaluated the 1 month out‐of‐sample volatility forecast performance of two statistical‐based models, Mixed Data Sampling (MIDAS) and GARCH, and compared the results with option‐implied volatility indexes. Our results suggest that MIDAS produce superior forecast performance compared to GARCH model and IV method. While options are not available for all assets, we believe that MIDAS model can be a sophisticated tool for researchers and analysts to forecast future volatility with its ability to process high‐frequency data. [ABSTRACT FROM AUTHOR]

High diamond price volatility can have significant impact on Botswana's diamond‐driven economy. The global economic crisis of 2008–2009 saw the local economy characterised by heightened commodity price uncertainty, falling stock prices and dwindling international demand for diamonds. In this paper we employ a number of techniques to analyse and assess the effect of diamond price volatility on stock returns in Botswana. Firstly, estimation of a Markov Switching model reveals that high volatility regimes in diamond prices have become more frequent and persistent since the recession. Secondly, a bivariate GARCH‐in‐Mean VAR model is estimated and the results recognize that diamond price volatility has a positive and significant influence on stock returns in Botswana. [ABSTRACT FROM AUTHOR]

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight‐choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1‐day‐ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility. [ABSTRACT FROM AUTHOR]

Using annual data over forty years from 1975 to 2014, this study investigates the gross domestic product (GDP) growth and the conditional volatility of foreign direct investment (FDI) in the Indian subcontinent. The study reports the rising trends of both FDI and GDP and documents that the greater-than-expected FDI due to innovative shocks or policy innovations positively influences conditional volatility of FDI which, in turn, positively contributes to the economic growth/development. The conditional volatility used in the study is the variance derived from the diagnostically selected exponential GARCH ( EGARCH) model. The study also reports the causality of both the FDI and its volatility across borders. More specifically, it reports bidirectional causality of FDI between India and Pakistan but unidirectional causality from Bangladesh to both India and Pakistan. It further documents that this volatility is persistent in all the economies and that it spillovers from both India and Pakistan to Bangladesh. The evidence attributes the economic development in the Indian subcontinent to the economic or policy innovations in attracting FDI. The findings of this study thus contribute to the literature by documenting the contrasting evidence that the volatility along with the trend of FDI contributes to the economic development and by reconciling the contrasting evidence. [ABSTRACT FROM AUTHOR]

Using a data set consisting of more than five years of 5-minute intraday stock index returns for major European stock indices and US macroeconomic surprises, conditional means and volatility behaviour in European markets were investigated. The findings suggest that the opening of the US stock market significantly raises the level of volatility in Europe, all markets responding in an identical fashion. Furthermore, US macroeconomic surprises exert an immediate and major impact on both the European stock markets' intraday returns and volatilities. Thus, high frequency data appear to be critical for the identification of news impacting the markets. [ABSTRACT FROM AUTHOR]

The diversifying power of inflation-linked (IL) bonds relative to traditional asset classes has changed significantly. In this paper, we study the dynamics of conditional volatilities and correlations for three asset classes, IL bonds, nominal bonds and equities, in the USA and Europe. Using a DCC-MVGARCH for the period 1997–2007, we highlight the change that took place in 2003. Although IL bonds once had definite diversification power, they are now highly correlated with nominal bonds and have reached similar volatility levels. As a result, the two asset classes are practically substitutable. This seems to be due to more stable inflation expectations and to a more liquid IL bond market. Although diversification was a valuable reason for introducing IL bonds in a global portfolio before 2003, this is no longer the case. Dynamic portfolio optimisation using our estimates of conditional correlations and volatilities clearly demonstrates that the optimal weight of IL bonds in a portfolio decreased sharply in 2003 in favour of nominal bonds and equities. [ABSTRACT FROM AUTHOR]

Jin and Frechette (2004) examined the degree to which agricultural price volatilities exhibited evidence of fractional integration and concluded it was important to consider both long-run and short-run memory when modeling conditional variances. The purpose of this note is to revisit the issue using new methods and techniques which generally reaffirm the view that return volatilities are fractionally integrated and conditionally heteroskedastic, with many exhibiting significant leverage effects, a result not previously reported. [ABSTRACT FROM AUTHOR]

Abstract: A new high frequency data set is used to estimate the impact of the Fed on the level and volatility of stock prices while accounting for endogeneity and omitted variable biases and potential asymmetries. Results show that after addressing these issues, the effect of policy shocks on the level and volatility of stock returns is higher than previously reported. GARCH findings indicate that the volatility impact is tent-shaped, spiking during policy announcements and declining before and after the release. The level and conditional volatility of stock returns is found to respond asymmetrically to the type of policy shocks (timing versus future path of monetary policy) and the type of policy action (easing versus tightening). [Copyright &y& Elsevier]

Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions and (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge fund indices themselves. We use the SNP approach to obtain estimates of conditional densities of hedge fund returns and then proceed to examine their properties. In general, a nonparametric GARCH(1,1) model appears to provide the best fit for all strategies. We find that the conditional third and fourth moments are significantly affected by changes in the current volatility of returns on hedge fund indices. We examine changes in the conditional probability of tail events and report significant changes in the probability of extreme events when the conditioning information changes. These results have important implications for models of hedge fund risk that rely on probability of tail events. We formally test for the presence of asymmetries in conditional correlations to determine if there is contagion between hedge funds and other investments and between various hedge fund indices in extreme down markets versus extreme up markets. We generally do not find strong evidence in support of asymmetric correlations. [ABSTRACT FROM AUTHOR]

The present paper investigates the characteristics of short-term interest rates in several countries. We examine the importance of nonlinearities in the mean reversion and volatility of short-term interest rates. We examine various models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate. We find that different markets require different models. In particular, we find evidence of nonlinear mean reversion in some of the countries that we examine, linear mean reversion in others and no mean reversion in some countries. For all countries we examine, there is strong evidence of the need for the volatility of interest rate changes to be highly sensitive to the level of the short-term interest rate. Out-of-sample forecasting performance of one-factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data. [ABSTRACT FROM AUTHOR]

This article tests whether the volatility of agricultural futures prices exhibits fractional integration. Volatility series were constructed for fourteen agricultural futures price series with over 5,300 observations per series. The volatility series exhibit strong long-term dependence, which is an indicator of fractional integration. A fractional integration model, FIGARCH(1, d, 1), performs significantly better than a traditional volatility model, GARCH(1,1), in modeling agricultural futures price volatility. [ABSTRACT FROM AUTHOR]

This paper investigates the robustness of a range of short-term interest rate models. We examine the robustness of these models over different data sets, time periods, sampling frequencies, and estimation techniques. We examine a range of popular one-factor models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate. We find that parameter estimates are highly sensitive to all of these factors in the eight countries that we examine. Since parameter estimates are not robust, these models should be used with caution in practice. [ABSTRACT FROM AUTHOR]

This paper applies the switching ARCH model introduced by Hamilton and Susmel (1994) to weekly DM/£ exchange rates for the period March 1987-December 1994. The sample period spans the UK's ERM tenure, which lasted until the currency crisis of September 1992. The SWARCH model generalizes standard ARCH models by allowing the conditional variance to experience jumps between discrete states or regimes. The SWARCH model is shown to provide a unified statistical framework for investigating two issues of interest in the literature: (i) the evolution of volatility and its implications for ERM credibility and (ii) the effects of regime shifts on the stochastic process governing conditional volatility. [ABSTRACT FROM AUTHOR]