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In this paper, we propose and evaluate a shrinkage based methodology that is designed to improve the accuracy of volatility forecasts. Our approach is based on a two-step procedure for extracting latent common volatility factors from a large dimensional and high-frequency dataset. In the first step, we apply either least absolute shrinkage operator (LASSO) or the elastic net (EN) shrinkage on estimated integrated volatilities, in order to select a subset of assets that are informative about the target asset. In the second step, we utilize (sparse) principal component analysis on the selected assets, in order to estimate latent return factors, which are in turn used to construct latent volatility factors. Our two-step method is found to yield more accurate volatility predictions than a variety of alternative models based on approaches such as direct application of (S)PCA and direct application of LASSO or EN shrinkage, when comparing out-of-sample R 2 s and mean absolute forecasting errors, and when implementing predictive accuracy tests. Additionally model confidence sets are found to contain models solely based on our two-step approach. These forecasting gains are found to be robust to the use of original or log-scale realized volatility models, different data sampling frequencies, and different forecasting sub-periods.

Modeling Bitcoin realized volatility by the heterogeneous autoregressive model is subject to substantial model specification uncertainty in practice. To circumvent the lag specification uncertainty, we introduce a new model averaging coefficient estimator with the mean squared error of the coefficient to be minimized. We show that the averaged coefficient vector has a root- n consistency with n being the sample size and propose using a double bootstrap to provide inference. Monte Carlo simulation results demonstrate reliability of the proposed method. The in-sample application shows that adjustment for measurement errors by HARQ-type models is necessary. The model averaging estimator has higher in-sample explanatory power with more significant predictors. The out-of-sample outcomes reveal that the forecast horizon plays a key role at determining the effectiveness of signed realized variance for predicting the Bitcoin volatility. Finally, the model averaging HARQ-type models demonstrate superior out-of-sample performance for both short and long forecast horizons.

We propose an autoregressive conditional duration (ACD) model with periodic time-varying parameters and multiplicative error form. We name this model periodic autoregressive conditional duration (PACD). First, we study the stability properties and the moment structures of it. Second, we estimate the model parameters, using (profile and two-stage) Gamma quasi-maximum likelihood estimates (QMLEs), the asymptotic properties of which are examined under general regularity conditions. Our estimation method encompasses the exponential QMLE, as a particular case. The proposed methodology is illustrated with simulated data and two empirical applications on forecasting Bitcoin trading volume and realized volatility. We found that the PACD produces better in-sample and out-of-sample forecasts than the standard ACD.

In this paper, we study the cryptocurrency asset pricing via machine learning. The common pricing factors similar to equity asset pricing are constructed. The classical equity-based risk factors including size, momentum, and value to growth from the Fama-French three factor model are studied. For volatility risk factor category, we investigate realized volatility, skewness and jump. We also investigate liquidity factors including bid-ask, volume growth and Roll’s measure. We collect other crypto-unique factors. We show the machine learning models which could handle 30 characteristics together explain most of the excess return of cryptocurrencies.

In this paper, we assess the dynamic impact of the U.S. monetary policy announcements on oil market futures returns and volatility. We use intra-day data for West Texas Intermediate (WTI) oil futures together with a time-varying modeling approach to study the nature of this dynamic impact. In addition, we also control for macroeconomic news shocks and separately study the response of good and bad realized volatility. Evidence suggests that there is a significant time variation in the response of oil returns, as well as its volatility to the Federal Reserve policy announcements. Broadly, we also find that higher (lower) uncertainty about Federal Reserve policy actions is associated with a weaker (stronger) impact of the surprise policy announcements on oil returns and volatility.

Using high frequency data and connectedness measures based on a time-varying parameter vector autoregression (TVP-VAR) model, we study dynamic connectedness among the realized volatility of 15 commodity futures (Gold, Heating oil, Light crude oil, Natural gas, Copper, Platinum, Cocoa, Coffee, Corn, Cotton, Orange Juice, Soybean, Soybean meal, Sugar, and Wheat) from September 22, 2008 to May 28, 2020. The results show strong and moderate levels of volatility connectedness among energy and metals and moderate connectedness levels within the group of agricultural commodities. Cross-commodity connectedness can explain a large portion of volatility connectedness in some cases, highlighting the importance of conducting realized volatility connectedness within a model allowing realized volatilities to be endogenously and simultaneously determined. Connectedness is robust to alternative specifications and varies with time. It is mostly driven by macroeconomic variables and uncertainty, including the term spread of interest rates and real economic activity. However, the analysis shows that some of the drivers of connectedness differ between upper and lower quantiles.

High frequency financial data has become an essential component of the digital economy, yielding an increasing number of estimators. However, it is hard to reliably assess the uncertainty of such estimators. The Observed Asymptotic Variance (observed AVAR) is a non-parametric estimator for (squared) standard error in high frequency data. The device is related to observed information in likelihood theory, but in this case it is non-parametric and uses the high-frequency data structure. An earlier paper has developed the estimator in the case where edge effects are small to moderate. In practical data, it is often more realistic to assume that edge effects can be large, and this is the problem that we tackle in the current paper. We here find a regression approach to observed AVAR which is highly robust to large edges. This approach covers most high frequency estimators.

This paper examines the informational content of Implied Volatility (IV) for 493 Malaysian and 945 Thai structured call warrants from 2014 to 2015. Unlike a regular warrant (issued by the firm) and attached to bonds, loan stocks, or preferred stocks as sweeteners, a structured warrant is issued by a third party. Structured warrant is a prevalent exchange-traded instrument in South-East Asian countries. This study is among the first to examine the IV of structured warrants using emerging market datasets from Bursa Malaysia and the Stock Exchange of Thailand. Our predictive regression models compare IV's effectiveness in anticipating future realized volatility vis-a-vis historical volatility. We record that IV in both exchanges contains relevant information about future realized volatility only on a handful of occasions. Additionally, structured warrants' IV is a mostly biased predictor with a diminutive efficiency threshold. Our findings bear implications for the region's derivatives market growth and risk management practices.

This manuscript extends the literature on the application of geometric Brownian motion. Forecasted drift and diffusion terms estimated separately and recursively are plugged into the framework to forecast S&P500 index values. Expected index values are estimated from 100,000 simulated index values and probabilities. The results of comparing expected index values with actual values indicate that although reliable predictions of S&P500 index values can be obtained at monthly, quarterly, and annual frequencies, the reliability may decrease in that order.

This paper presents a tractable dynamic equilibrium model of stock return extrapolation in the presence of stochastic volatility. In the model, consistent with survey evidence, investors expect future returns to be higher (lower) but also less (more) volatile following positive (negative) stock returns. The biased volatility expectation introduces a new channel through which past returns and investor sentiment affect derivative prices. In particular, through this novel channel, the model reconciles the otherwise puzzling evidence of past returns affecting option prices and the evidence of variance risk premium predicting future stock market returns even after controlling for the realized variance.

We examine a continuous-time principal-agent problem under mean-volatility joint ambiguity uncertainties. Both the principal and the agent exhibit Gilboa–Schmeidler’s extreme ambiguity aversion with exponential utilities. We distinguish between expost realized and exante perceived volatilities, and argue that the second-best contract necessarily consists of two sharing rules: one for realized outcome and the other for realized volatility. The outcome-sharing rule is for uncertainty sharing and work incentives, as usual, and the volatility-sharing rule is to align the agent’s worst prior with that of the principal. At optimum, their worst priors are symmetrized, and realized compensation is positively related to realized volatility. This theoretical positive relation can be consistent with popular managerial compensation practices such as restricted stock plus stock option grants. A closed-form solution to a linear-quadratic example is provided.

We use intraday data to construct measures of the realized volatility of bitcoin returns. We then construct measures that focus exclusively on relatively large realizations of returns to assess the...

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.

We present a new model to decompose total daily return volatility into high-frequency-based open-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to obtain robust volatility dynamics. Applying our new model to a 2001–2018 sample of individual stocks and stock indices, we find substantial in-sample variation of the daytime-to-total volatility ratio over time. We apply the model to out-of-sample forecasting, evaluated in terms of Value-at-Risk and Expected Shortfall. Models with a non-constant volatility ratio typically perform best, particularly in terms of Value-at-Risk. Our new model performs especially well during turbulent times. All results are generally stronger for individual stocks than for index returns.

We forecast monthly realized volatility (RV) of the oil price based on an extended heterogenous autoregressive (HAR)-RV model that incorporates the role of the El Niño Southern Oscillation (ENSO), as captured by the Equatorial Southern Oscillation Index (EQSOI). Based on the period covering 1986 January to 2020 December and studying various rolling-estimation windows and forecast horizons, we find that the EQSOI has predictive value for oil-price RV particularly at forecast horizons from 2 to 4 years, and for rolling-estimation windows of length 4 to 6 years. We show that this result holds not only based on standard tests of out-of-sample predictability, but also under an asymmetric loss function.

The evidence is mixed regarding the role of short sellers on stock market efficiency, with the majority of studies assessing short selling activities during abnormal market conditions. This study investigates the effect of short selling on stock volatility during normal market conditions in the Australian stock market using various proxies for volatility and trading activities. While short volume does not supplant the number of trades in the volume and volatility relationship, our results suggest that short selling has some incremental positive effects on volatility. Overall, our vector autoregression (VAR) analysis suggests that trading by short sellers increases volatility even during normal market conditions. JEL Classification: G10, G12, G13

It is well known that realized measures of volatility, which are computed from high-frequency intraday data, provide accurate measurements of the latent volatility process. We investigate the role of realized volatility in pricing VIX options using the GARV model (Christoffersen et al., 2014), and the Realized GARCH model (Hansen et al., 2012). A closed-form pricing formula for the (affine) GARV model is developed. For the (non-affine) log-linear Realized GARCH model, we introduce a novel approximation approach to derive its analytical pricing formula. We show that the newly proposed approximation method is fast, with a high degree of accuracy. An extensive empirical application on VIX options from 2006 to 2020 shows that models with realized volatility significantly outperform conventional GARCH-type models based on daily returns only. Among these, the Realized GARCH model provides the best pricing performance due to its fewer constraints and a more flexible modeling structure. Our results hold both in-sample and out-of-sample.

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.

The implied volatility for 50 ETF options in China shows a significant smile pattern across different moneyness. Call and put options on 50 ETFs transacted from February 2015 to December 2018 are obtained. Regression and vector autoregression analyses are employed to investigate the structural relationship between the volatility smile and potential determinants. The determinants considered in this study include liquidity variables, market momentum, features of the underlying asset and time to maturity. The empirical results show that time to maturity and uncertainty of the underlying asset are the main determinants of the structure of the volatility smile. Moreover, realized volatility of the underlying asset and liquidity proxy variables, open interest and the trading volume of options contracts, have significant impact on the structure of the volatility smile.

This study employs the generalized autoregressive conditional heteroscedasticity-mixed data sample framework to examine the role of oil market shocks in determining the long-term volatility of cryptocurrencies. We analyzed three cryptocurrencies, namely, Bitcoin, Ethereum, and XRP, and six oil market shocks, namely, oil price return (OR), oil realized volatility (ORV), oil realized skewness (ORSV), oil supply shocks (OSS), and oil demand shocks [global aggregate demand for industrial commodities (OADS) and crude oil market demand shocks (OSDS)]. The empirical results of the study reveal that OR, ORV, ORSV, and the two types of oil demand shocks (OADS and OSDS) and OSS have negative and positive impacts, respectively, on the long-term volatility of cryptocurrencies. We further gained insights on the economic underpinnings and observed that the ability of the oil market shocks to signal the uncertainty of the macroeconomic environment is clearly the most important source of its negative and significant impact on the volatility of cryptocurrencies. Moreover, an adverse oil market shock increases the attractiveness of cryptocurrencies, as they could provide shelter from sovereign risk and weakness. Overall, our study highlights a clear and comprehensive understanding of the association between the oil market risk and the cryptocurrency volatility dynamic and the possibility for cryptocurrencies to act as a hedge under specific uncertain economic conditions.

We investigate price duration variance estimators that have long been neglected in the literature. In particular, we consider simple-to-construct non-parametric duration estimators, and parametric price duration estimators using autoregressive conditional duration specifications. This paper shows (i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and (ii) how they are affected by discrete and irregular spacing of observations, market microstructure noise, and finite price jumps. Specifically, we contribute to the literature by constructing the asymptotic theory for the non-parametric estimator with and without the presence of bid/ask spread and time discreteness. Further, we provide guidance about how our estimators can best be implemented in practice by appropriately selecting a threshold parameter that defines a price duration event, or by averaging over a range of non-parametric duration estimators. We also provide simulation and forecasting evidence that price duration estimators can extract relevant information from high-frequency data better and produce more accurate forecasts than competing realized volatility and option-implied variance estimators, when considered in isolation or as part of a forecasting combination setting.

This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities — in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price equity volatility risk using factors which go beyond the equity class namely Volatility Factors extracted from panels of volatilities of short-run funding spreads. The role of these Volatility Factors is compared with the corresponding factors extracted from the panels of the above spreads as well as related factors proposed in the literature. Our monthly short-run funding spreads Volatility Factors provide both in- and out-of-sample predictive gains for forecasting the monthly VIX, RV as well as the equity premium, while the corresponding daily volatility factors via Mixed Data Sampling (MIDAS) models provide further improvements.

This study tests and documents the information content of all publicly available implied volatility indices regarding both the realized volatility and the returns of the underlying asset. These topics present a path traveled by earlier work, but there are gains in studying together all 47 volatility-based indices that are now available, in order to examine if different asset classes and financial instruments could possess different return-volatility relations and forecasting ability. Our findings suggest that implied volatility includes information about future volatility beyond that contained in past volatility; this finding is consistent across all assets under review. Furthermore, we find a significant contemporaneous relationship between implied volatility changes and underlying returns, but at the same time, we show that implied volatilities in commodities, bonds, currencies and volatility react differently to underlying price changes compared to equities. Hence, our findings have important implications for asset allocation, risk management and asset pricing.

La classe des modèles à chaîne de Markov cachée (HMM, Hidden Markov Models) permet, entre autres, de modéliser des données financières. Par exemple, dans ce type de modèle, la distribution du rendement sur un actif financier est exprimée en fonction d'une variable non-observée, une chaîne de Markov, qui représente la volatilité de l'actif. Notons que les dynamiques de cette volatilité sont difficiles à reproduire, car la volatilité est très persistante dans le temps. Les HMM ont la particularité de permettre une variation de la volatilité selon les états de la chaîne de Markov. Historiquement, ces modèles ont été estimés avec un nombre faible de régimes (états), car le nombre de paramètres à estimer explose rapidement avec le nombre de régimes et l'optimisation devient vite difficile. Pour résoudre ce problème une nouvelle sous-classe de modèles à chaîne de Markov cachée, dite à haute dimension, a vu le jour grâce aux modèles dits factoriels et à de nouvelles méthodes de paramétrisation de la matrice de transition. L'objectif de cette thèse est d'étendre cette classe de modèles avec de nouvelles approches plus générales et de montrer leurs applications dans le domaine financier. Dans sa première partie, cette thèse formalise la classe des modèles factoriels à chaîne de Markov cachée et étudie les propriétés théoriques de cette classe de modèles. Dans ces modèles, la dynamique de la volatilité dépend d'une chaîne de Markov latente de haute dimension qui est construite en multipliant des chaînes de Markov de dimension plus faible, appelées composantes. Cette classe englobe les modèles factoriels à chaîne de Markov cachée précédemment proposés dont les composantes sont de dimension deux. Le modèle MDSV (Multifractal Discrete Stochastic Volatility) est introduit afin de pouvoir considérer des composantes de dimension supérieure à deux, généralisant ainsi les modèles factoriels existants. La paramétrisation particulière de ce modèle lui offre suffisamment de flexibilité pour reproduire différentes allures de décroissance de la fonction d'autocorrélation, comme celles qui sont observées sur les données financières. Un cadre est également proposé pour modéliser séparément ou simultanément les données de rendements financiers et de variances réalisées. Une analyse empirique sur 31 séries d'indices financiers montre que le modèle MDSV présente de meilleures performances en termes d'estimation et de prévision par rapport au modèle realized EGARCH. La modélisation par l'entremise des modèles factoriels à chaîne de Markov cachée nécessite qu'on définisse le nombre N de composantes à multiplier et cela suppose qu'il n'existe pas d'incertitude lié à ce nombre. La seconde partie de cette thèse propose, à travers une approche bayésienne, le modèle iFHMV (infinite Factorial Hidden Markov Volatility) qui autorise les données à déterminer le nombre de composantes nécessaires à leur modélisation. En s'inspirant du processus du buffet indien (IBP, Indian Buffet Process), un algorithme est proposé pour estimer ce modèle, sur les données de rendements financiers. Une analyse empirique sur les données de deux indices financiers et de deux actions permet de remarquer que le modèle iFHMV intègre l'incertitude liée au nombre de composantes pour les estimations et les prévisions. Cela lui permet de produire de meilleures prévisions par rapport à des modèles de référence., Hidden Markov Models (HMMs) are popular tools to interpret, model and forecast financial data. In these models, the return dynamics on a financial asset evolve according to a non-observed variable, a Markov chain, which generally represents the volatility of the asset. This volatility is notoriously difficult to reproduce with statistical models as it is very persistent in time. HMMs allow the volatility to vary according to the states of a Markov chain. Historically, these models are estimated with a very small number of regimes (states), because the number of parameters to be estimated grows quickly with the number of regimes and the optimization becomes difficult. The objective of this thesis is to propose a general framework to construct HMMs with a richer state space and a higher level of volatility persistence. In the first part, this thesis studies a general class of high-dimensional HMMs, called factorial HMMs, and derives its theoretical properties. In these models, the volatility is linked to a high-dimensional Markov chain built by multiplying lower-dimensional Markov chains, called components. We discuss how previously proposed models based on two-dimensional components adhere to the factorial HMM framework. Furthermore, we propose a new process---the Multifractal Discrete Stochastic Volatility (MDSV) process---which generalizes existing factorial HMMs to dimensions larger than two. The particular parametrization of the MDSV model allows for enough flexibility to reproduce different decay rates of the autocorrelation function, akin to those observed on financial data. A framework is also proposed to model financial log-returns and realized variances, either separately or jointly. An empirical analysis on 31 financial indices reveals that the MDSV model outperforms the realized EGARCH model in terms of fitting and forecasting performance. Our MDSV model requires us to pre-specify the number of components and assumes that there is no uncertainty on that number. In the second part of the thesis, we propose the infinite Factorial Hidden Markov Volatility (iFHMV) model as part of a Bayesian framework to let the data drive the selection of the number of components and take into account the uncertainty related to the number of components in the fitting and forecasting procedure. We also develop an algorithm inspired by the Indian Buffet Process (IBP) to estimate the iFHMV model on financial log-returns. Empirical analyses on two financial indices and two stocks show that the iFHMV model outperforms popular benchmarks in terms of forecasting performance.

This paper shows that generalizing the heterogeneous autoregressive model (HAR) with realized (co)variances and semi-(co)variances from the index leads to more accurate volatility forecasts. To circumvent the effects of the market microstructure noise arising from using high sampling frequencies, we adopt noise-robust estimators for the realized (co)variances and develop novel noise-robust estimators for the semi-(co)variances. To explore the sampling frequency at which the forecasting gains are maximized, we adopt a mixed-sampling approach that iterates over several sampling frequencies of the stock and the index. Our analysis shows that gains are maximized at the combination of a low (high) frequency on the stock (index). We illustrate that the observed forecasting gains translates into economic gains such that a risk-averse investor is willing to pay up to 57 annual basis points by adopting a model specification that utilizes the index information.

This paper shows that shocks to the equity capital ratio of financial intermediaries (CRFI) have predictive ability for stock realized volatility, from both in‐sample and out‐of‐sample perspectives. The revealed predictability is also of economic significance, in that it examines the performance of portfolios constructed on the basis of CRFI forecasts of stock volatility. Robustness test results suggest that CRFI provides different information from traditional macro variables. Further analysis shows that simple linear regression is good enough in capturing predictive relationships between CRFI and stock volatility.

In several financial applications, it is extremely useful to predict volatility with the highest precision. Neural Networks alongside GARCH-type models have been extensively employed in the last decades for estimating volatility of financial indices. The motivation of this survey is to decide whether combining different types of models can improve the return volatility forecasts. Thus, two hybrid models are utilized and compared with an asymmetric GARCH model and a Neural Network in terms of their ability to predict the volatility of the FTSE MIB index. The conclusions reveal that the hybrid model, which is based on a Neural Network having as inputs the returns and its historical values as well as the estimates of conditional volatility obtained by an EGARCH model, provides the best predictive power. Moreover, the dominance of this hybrid model is such that it forecast encompasses the remaining models. Finally, it is demonstrated that there are significant leverage effects in the Italian stock market.Keywords: Artificial Neural Network; Forecast Encompassing; GARCH Models; Realized volatility; Stock Market; Volatility ForecastJEL Classifications: C22; C45; C53; G15DOI: https://doi.org/10.32479/ijefi.10842

This article investigates whether the nonlinear support vector regression method under the Heterogeneous Auto-Regressive model (SVR-HAR) can compete for combination methods in terms of out-of-sampl...

We use heterogeneous autoregression (HAR) and two related HAR extension models to examine volatility forecasting performances before and after the launch of night trading sessions in the Shanghai Futures Exchange (SHFE) gold futures market. To capture fluctuations from external information and volatility of realized volatility (RV), we incorporate the trading volume and jumping into the HAR‐V‐J model in the first place and then incorporate a GARCH specification into the HAR‐GARCH model. Results showed that there were large fluctuations in SHFE gold futures market before the launch of night trading sessions and mostly stemmed from overnight fluctuation in the international gold futures market. After the launch of night trading sessions, the realized volatility has a clear trend of moderation. In the in‐sample estimation, both jump and external information are found to have significant explanatory power with the HAR‐V‐J model. Additionally, the volatility clustering and high persistence of the realized volatility were confirmed by the GARCH coefficients. Last but not the least, night trading sessions have significantly improved the out‐of‐sample forecasting performances of realized volatility models. Among them, the HAR‐V‐J model is the best‐performing model. This conclusion holds for various prediction horizons and has great practical values for investors and policymakers.

Using a new quarterly panel of corporate, institutional, and macroeconomic data, we investigate the impact of economic property rights, stock market returns and stock market realized volatility as ...

We model future EUR/USD exchange rate realized volatility (RV) within a class of heterogeneous autoregressive (HAR) models augmented by implied volatilities (IVs). The existing literature has almost unanimously employed IVs from options with one-month maturities; however, our in-sample analysis shows that using IVs from options with a shorter maturity (of one day and one week) might be more relevant when explaining the volatility of the next day and week. In general, IVs are more useful in predicting future RV than past RVs (daily, weekly and monthly averages). At the same time, RVs seem to contain only small incremental predictive power compared to IVs. The out-of-sample results strengthen our in-sample results, as they show the increased predictive power of the models with implied volatility up to 17.3% for one-day-ahead, 42.1% for one-week-ahead, and 22.8% for one-month-ahead forecasts. Additionally, the superior set of models contains only volatility model specifications with IVs. Our results hold not only for individual forecast models but also for combinations of volatility forecasts. We show that increased forecasting accuracy is stable across time and that it is achieved during periods of high market volatility. Our study also provides new evidence that implied volatility from short-lived options as a serious contender for modeling realized volatility We model future EUR/USD exchange rate realized volatility (RV) within a class of heterogeneous autoregressive (HAR) models augmented by implied volatilities (IVs). The existing literature has almost unanimously employed IVs from options with one-month maturities; however, our in-sample analysis shows that using IVs from options with a shorter maturity (of one day and one week) might be more relevant when explaining the volatility of the next day and week. In general, IVs are more useful in predicting future RV than past RVs (daily, weekly and monthly averages). At the same time, RVs seem to contain only small incremental predictive power compared to IVs. The out-of-sample results strengthen our in-sample results, as they show the increased predictive power of the models with implied volatility up to 17.3% for one-day-ahead, 42.1% for one-week-ahead, and 22.8% for one-month-ahead forecasts. Additionally, the superior set of models contains only volatility model specifications with IVs. Our results hold not only for individual forecast models but also for combinations of volatility forecasts. We show that increased forecasting accuracy is stable across time and that it is achieved during periods of high market volatility. Our study also provides new evidence that implied volatility from short-lived options as a serious contender for modeling realized volatility

In this paper, we suggest how to handle the issue of the heteroskedasticity of measurement errors when specifying dynamic models for the conditional expectation of realized variance. We show that either adding a GARCH correction within an asymmetric extension of the HAR class ( AHAR - GARCH ), or working within the class of asymmetric multiplicative error models ( AMEM ) greatly reduces the need for quarticity/quadratic terms to capture attenuation bias. This feature in AMEM can be strengthened by considering regime specific dynamics. Model Confidence Sets confirm this robustness both in- and out-of-sample for a panel of 28 big caps and the S&P500 index.

This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed‐Data Sampling (GARCH‐MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH‐MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH‐MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and (3) high‐frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH‐MIDAS in volatility forecasting.

Apart from the percentage change in the price of crude oil, there is a growing tradition of using various nonlinear transformations of the price of crude oil to forecast real gross domestic product growth rates, equity returns, inflation and other macroeconomic variables. This study attempts to quantify the additional potential predictive power afforded by crude oil price volatility relative to widely used crude oil price‐based variables for more than 300 US macroeconomic time series at the monthly and the quarterly sampling frequency. We observe that regressions employing crude oil price realized volatility and crude oil price realized semivolatilities tend to afford a more consistent pattern of out‐of‐sample prediction gains relative to competitors using well‐known crude oil price measures and the autoregressive benchmark at the quarterly and monthly sampling frequency. While it is somewhat harder to find evidence of finite‐sample predictive gains relative to the benchmark, the evidence is stronger with respect to population‐level predictability 1 quarter (1 month) ahead for the model with crude oil price realized semivolatilities across the considered data and models. Furthermore, point (density) forecasts employing crude oil price realized volatility tend to be more accurate than corresponding forecasts produced under the crude oil price‐based predictive regressions in a horse race.

This paper examines the information content of implied volatility of structuredcall warrants in the Singapore Stock Exchange. The study is among the first to examine the implied volatility of equity options (structured call warrants) outside the United States. Using a daily dataset for 252 trading days between August 1, 2014 and July 31, 2015, we test whether implied volatility is an unbiased estimate of realized volatility (RV). In other words, we ask whether implied volatility contains information on future RV, and scrutinize the efficiency of implied volatility and its predictive power compared to historical volatility (HV). Our findings suggest that although implied volatility does contain some relevant information about future volatility, it remains a biased forecast of RV. The efficiency of implied volatility is trivial, and its predictive power is not superior to HV.

Theoretically, there is exists a strong link between monetary policy rate uncertainty and equity return volatility, since asset pricing models assume the risk-free rate to be a key factor for equity prices. Most studies, however, focus on aggregate volatility proxies, ignoring the evidence that idiosyncratic risk could also be an important consideration, particularly for under-diversified investors and arbitrageurs. Given this, we examine firm-level annual data for the United States over 1997 to 2016, and show that monetary policy uncertainty does indeed contain significant predictive information over realized and implied volatilities at both the firm- and industry-level. The predictive power of monetary policy uncertainty is found to be robust across the low and high quantiles of volatility with higher policy uncertainty predicting higher firm-level volatility in subsequent periods. While the strongest possible volatility effect is observed in the case of Retail Trade, we observe opposite resuThis paper provides novel evidence for the This paper provides novel evidence for the predictive power of monetary policy uncertainty (MPU) over stock return volatility at the firm level based on a dataset constructed from 9,458 U.S. firms. Our findings show that monetary policy uncertainty contains significant predictive information over realized and implied volatilities at both the firm- and industry-level, with higher policy uncertainty predicting higher volatility in subsequent periods. While the strongest possible volatility effect is observed in the case of Retail Trade, we observe opposite results for Mining with high policy uncertainty predicting lower volatility in this sector. We argue that the dual nature of the underlying commodity for Mining companies, both as a consumption and investment asset, drives the negative effect of policy uncertainty on volatility in this sector. Nevertheless, the findings highlight the predictive information captured by monetary policy actions on the idiosyncratic component of equity market volatility.predictive power of monetary policy uncertainty (MPU) over stock return volatility at the firm level based on a dataset constructed from 9,458 U.S. firms. Our findings show that monetary policy uncertainty contains significant predictive information over realized and implied volatilities at both the firm- and industry-level, with higher policy uncertainty predicting higher volatility in subsequent periods. While the strongest possible volatility effect is observed in the case of Retail Trade, we observe opposite results for Mining with high policy uncertainty predicting lower volatility in this sector. We argue that the dual nature of the underlying commodity for Mining companies, both as a consumption and investment asset, drives the negative effect of policy uncertainty on volatility in this sector. Nevertheless, the findings highlight the predictive information captured by monetary policy actions on the idiosyncratic component of equity market volatility.lts for Mining with high policy uncertainty predicting lower volatility in this sector. We argue that the dual nature of the underlying commodity for Mining companies, both as a consumption and investment asset, drives the negative effect of policy uncertainty on volatility in this sector. Nevertheless, the findings highlight the predictive information captured by monetary policy actions on the idiosyncratic component of equity market volatility.

We examine the predictive power of disentangled oil price shocks over gold market volatility via the heterogeneous autoregressive realized volatility (HAR-RV) model. Our in- and out-of-sample tests...

Using the nearest neighbor truncation (NNT) approach, this study investigates the realized volatility transmission between the Malaysian Islamic market with various global sectoral Islamic stock markets by extending the heterogeneous autoregressive (HAR) with GARCH, asymmetric effects and jump-robust volatility estimator established in a multivariate setting. The multivariate HAR-GARCH model is capable to capture the persistence and time-varying volatility of realized volatility while NNT approach allows for an asymptotic limit theory in the presence of jumps. Using intraday data, the findings suggest the daily realized volatilities of the Islamic equities rely significantly on their own short-, mid- and long-term volatility components. Besides, there is an evidence of volatility spillover in majority of the pairwise but with low magnitude. The findings also confirm the vital role of conditional heteroscedasticity in the volatility series in explaining the measured volatility transmission of the Islamic stock indices. Hence, this study provides useful insight to understand the portfolio risk of Shariah-compliant equities in making better-informed portfolio allocation.

This study aims to investigate which types of commodity price information are more useful for predicting US stock market realized volatility (RV) in a data-rich word. The standard predictive regression framework and monthly RV data are used to explore the RV predictability of commodity futures for the next-month RV on S&P 500 spot index. We utilize principal component analysis (PCA) and factor analysis (FA) to extract the common factors for each type and all types of commodity futures. Our results indicate that the futures volatility information of grains and softs has a significant predictive ability in forecasting the RV of the S&P 500. In addition, the FA method can yield better forecasts than the PCA and average methods in most cases. Further analysis shows that the volatility information of grains and softs exhibits higher informativeness during recessions and pre-crises. Finally, the forecasts of the five combination methods and different out-of-sample periods confirm our results are robust.

The main problem in volatility forecasting is that the variable of interest is unobservable, which complicates not only the construction of forecasts but also their comparison. This article challenges the common practice of using only proxy-robust loss functions, which have the nice property that they lead to the same ranking of forecasts regardless whether the unobservable true volatility is used or some unbiased proxy. It is shown that two proxy-robust loss functions need not necessarily produce similar rankings but may even produce completely contradictory rankings. Two likelihood-based loss functions are proposed instead, which are not exactly proxy-robust but are still robust in the classical sense. The first is based on a t-distribution and is meant for daily data. The second is based on an F-distribution and is meant for high-frequency data. In the latter case, the squared error loss function may also be used when a logarithmic transformation is applied to the realized variances in order to achieve approximate normality. An alternative transformation is proposed which allows the adaptation to the degree of non-normality. The forecasting procedures that are compared by the different loss functions include GARCH, HAR, HARQ, and MIDAS models as well as nonparametric techniques. Finally, the economic relevance of choosing the right forecast is illustrated with the problem of establishing the intertemporal risk–return tradeoff. All theoretical arguments are backed up with empirical evidence obtained from daily data as well as from high-frequency data.

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

In this article, the authors propose a novel option strategy that is better (in terms of success rate, profitability, risk, and return) than the short straddle option strategy on the Indian stock market index, Nifty. By using the trigonometric ratio tan θ, an angle is formed between the monthly points earned/lost by the short straddle option strategy and the monthly change in the price of the Nifty index. Further, the angle for the next month is forecast by using the technique of the 14-month moving average. After calculating the forecasted angle by the 14-month moving—average technique, the related angle (of forecasted angle) in different quadrants is calculated. The angles of quadrants one and two are considered because short straddle is profitable in quadrants one and two and hence the angles of quadrants three and four have been ignored. By using the total straddle premium of the forecasting month, the upside and downside levels of Nifty of that month were calculated where this forecast angle will form. After finding the upper level and lower level of Nifty, the authors propose a strategy (angled short straddle strategy) that involves the selling of a call and put option. They compare the proposed strategy against the short straddle option strategies based on the success rate, overall profitability, risk, and return in the Indian stock market. The result indicates that the proposed strategy has an 18.06% higher success rate than the short straddle strategy. In terms of overall profitability, the proposed option strategy is more profitable by 57.22% than the short straddle strategy. They also compare the risk and return of both strategies, and their results show that the proposed strategy has lower risk and higher return than the short straddle option strategy; these results were verified by the regression analysis. In the end, the results of the success rate have been verified by the time-series plot of India VIX and realized volatility. Due to the higher success rate and better profitability, the proposed option strategy can be consistently applied by any trader on the Nifty for profit generation. TOPICS:Options, emerging markets, performance measurement, wealth management Key Findings ▪ The angled short straddle option strategy is more successful and profitable than the short straddle option strategy in the Indian stock Index (Nifty). ▪ The Nifty index has less bullish than bearish volatility behavior. ▪ The Nifty index has range-bound properties. ▪ This is the first article where trading results are based on angle tan θ.