•A negative relationship between the VIX index and the S&P 500 index return.•A positive contemporaneous link with the volume of the S&P 500 index.•The term spread has a slightly negative long-run ...impact in the VIX index.•Increases in the value of the US dollar tend to move down options-implied market volatility.
This paper performs a thorough statistical examination of the time-series properties of the daily market volatility index (VIX) from the Chicago Board Options Exchange (CBOE). The motivation lies not only on the widespread consensus that the VIX is a barometer of the overall market sentiment as to what concerns investors’ risk appetite, but also on the fact that there are many trading strategies that rely on the VIX index for hedging and speculative purposes. Preliminary analysis suggests that the VIX index displays long-range dependence. This is well in line with the strong empirical evidence in the literature supporting long memory in both options-implied and realized variances. We thus resort to both parametric and semiparametric heterogeneous autoregressive (HAR) processes for modeling and forecasting purposes. Our main findings are as follows. First, we confirm the evidence in the literature that there is a negative relationship between the VIX index and the S&P 500 index return as well as a positive contemporaneous link with the volume of the S&P 500 index. Second, the term spread has a slightly negative long-run impact in the VIX index, when possible multicollinearity and endogeneity are controlled for. Finally, we cannot reject the linearity of the above relationships, neither in sample nor out of sample. As for the latter, we actually show that it is pretty hard to beat the pure HAR process because of the very persistent nature of the VIX index.
In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. ...Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high‐frequency financial data.
Inflation forecasting is an important but difficult task. Here, we explore advances in machine learning (ML) methods and the availability of new datasets to forecast U.S. inflation. Despite the ...skepticism in the previous literature, we show that ML models with a large number of covariates are systematically more accurate than the benchmarks. The ML method that deserves more attention is the random forest model, which dominates all other models. Its good performance is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation.
Supplementary materials
for this article are available online.
Optimal pricing, that is determining the price level that maximizes profit or revenue of a given product, is a vital task for the retail industry. To select such a quantity, one needs first to ...estimate the price elasticity from the product demand. Regression methods usually fail to recover such elasticities due to confounding effects and price endogeneity. Therefore, randomized experiments are typically required. However, elasticities can be highly heterogeneous depending on the location of stores, for example. As the randomization frequently occurs at the municipal level, standard difference-in-differences methods may also fail. Possible solutions are based on methodologies to measure the effects of treatments on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls. For example, for each city in the treatment group, a counterfactual may be constructed from the untreated locations. In this article, we apply a novel high-dimensional statistical method to measure the effects of price changes on daily sales from a major retailer in Brazil. The proposed methodology combines principal components (factors) and sparse regressions, resulting in a method called Factor-Adjusted Regularized Method for Treatment evaluation (FarmTreat). The data consist of daily sales and prices of five different products over more than 400 municipalities. The products considered belong to the sweet and candies category and experiments have been conducted over the years of 2016 and 2017. Our results confirm the hypothesis of a high degree of heterogeneity yielding very different pricing strategies over distinct municipalities.
Supplementary materials
for this article are available online.
Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single "treated" unit suffers an intervention, such as a policy ...change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of "untre ated" peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual analysis for high-dimensional, nonstationary data with either deterministic and/or stochastic trends, which nests well-established methods, such as the synthetic control. We propose a resampling procedure to test intervention effects that does not rely on postintervention asymptotics and that can be used even if there is only a single observation after the intervention. A simulation study is provided as well as an empirical application.
Supplementary materials
for this article are available online.
Realized Volatility: A Review McAleer, Michael; Medeiros, Marcelo C.
Econometric reviews,
01/2008, Letnik:
27, Številka:
1-3
Journal Article, Book Review
Recenzirano
Odprti dostop
This article reviews the exciting and rapidly expanding literature on realized volatility. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time ...model is presented in order to motivate the main results. A continuous time specification provides the theoretical foundation for the main results in this literature. Cases with and without microstructure noise are considered, and it is shown how microstructure noise can cause severe problems in terms of consistent estimation of the daily realized volatility. Independent and dependent noise processes are examined. The most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given. The finite sample properties are discussed in comparison with their asymptotic properties. A multivariate model is presented to discuss estimation of the realized covariances. Various issues relating to modelling and forecasting realized volatilities are considered. The main empirical findings using univariate and multivariate methods are summarized.
The chloride penetration in three different exposure zones (Atmospheric, Splash and Tidal) of an offshore concrete platform in the year 2000 and 2005 was analyzed. Chlorides profiles for different ...orientations of the analyzed structure were also obtained. The apparent diffusion coefficients and surface chloride contents of concrete specimens were determined by curve fitting of chloride profiles in chloride penetration models based in diffusion. Increase in the chlorides ingress with exposure time was verified and microclimatic factors such as exposure to wind and wetting and drying cycles were the main responsible for the behavior of obtained chloride profiles.
•We discuss the chloride penetration in three different exposure zones.•Chlorides profiles in the year 2000 and 2005 were analyzed.•Increase in the chlorides ingress with exposure time was verified.•Microclimate is important for the behavior of obtained chloride profiles.•Wind and wetting and drying cycles affects the chloride diffusion coefficient.
Chloride is one of the main factors responsible for damages related to the corrosion of the concrete reinforcement in marine environments. It is known that this mechanism of degradation is directly ...related to environmental variables. Within this context, it can be inserted the global climate change. This paper deals with the effects of temperature and relative humidity changes on the service life of concrete structures affected by chloride attack. This way, three situations of environmental aggressiveness were simulated: past, current, and future. Then, models for predicting the chlorides penetration were analyzed to the three selected situations. So, a practical methodology is presented, and the results are consistent with the literature data. Among the results, it can be noted that changes in temperature and relative humidity identified in a period of 100 years were responsible for a reduction from 7.8 to 10.2 years of service life. Most standards provide a design service life of 50 years for reinforced concrete structures.
We consider a new, flexible and easy-to-implement method to estimate thecausal effects of an intervention on a single treated unit when a control group is not available and which nests previous ...proposals in the literature. It is a two-step methodology where in the first stage, a counterfactual is estimated based on a large-dimensional set of variables from a pool of untreated units by means of shrinkage methods, such as the least absolute shrinkage and selection operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. We show that these results hold even when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are derived. By a simple transformation of the variables, it is possible to test for multivariate intervention effects on several moments of the variables of interest. Existing methods in the literature usually test for intervention effects on a single variable and assume that the time of the intervention is known. In addition, high-dimensionality is frequently ignored and inference is either conducted under a set of more stringent hypotheses and/or by permutation tests. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives. As an application, we evaluate the effects on inflation, GDP growth, retail sales and credit of an anti tax-evasion program.
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