Dynamic volatility spillover effects between oil and agricultural products

The strong volatility spillover between crude oil and agricultural commodity markets reduces the diversification benefits and implies costly risk management process faced by portfolio managers and agricultural producers. This paper proposes a comprehensive study of their dynamic implied volatility spillover effects after the global financial crisis 2008–2009, while considering the transition between oil volatility's regimes. By using implied volatility, our analysis emphasizes on the forward-looking information that market traders usually convey in making decisions. We employ the generalized spillover indices within a fractionally integrated VAR model to capture the dynamic patterns of the volatility spillover effects alongside the Markov Switching Autoregressive model to extract the regimes of oil. Our results show new evidence that the net volatility spillover effect from crude oil to all agricultural commodities tends to decrease when crude oil remains in its low volatility regime. Conversely, this effect experiences an increasing trend when crude oil remains in its relatively high volatility regime. A dynamic strategy that combines oil and the most balanced agricultural commodity in terms of volatility transmission with oil (i.e., close-to-zero net volatility spillovers) depending on oil's regimes consistently outperforms the buy and hold strategy in terms of information ratio.     

The impact of unconventional monetary policy on the market for collateral: The case of the French bond market

We consider the channel consisting in transferring the credit risk associated with refinancing operations between financial institutions to market participants. In particular, we analyze liquidity and volatility premia on the French government debt securities market, since these assets are used as collateral both in the open market operations of the ECB and on the interbank market. In our time-varying transition probability Markov-switching (TVTP-MS) model, we highlight the existence of two regimes. In one of them, which we refer to as the conventional regime, monetary policy neutrality is verified; in the other, which we dub the unconventional regime, monetary policy operations lead to volatility and liquidity premia on the collateral market. The existence of these conventional and unconventional regimes highlights some asymmetries in the conduct of monetary policy.     

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.     

Option pricing under fast-varying and rough stochastic volatility

Recent empirical studies suggest that the volatilities associated with financial time series exhibit short-range correlations. This entails that the volatility process is very rough and its autocorrelation exhibits sharp decay at the origin. Another classic stylistic feature often assumed for the volatility is that it is mean reverting. In this paper it is shown that the price impact of a rapidly mean reverting rough volatility model coincides with that associated with fast mean reverting Markov stochastic volatility models. This reconciles the empirical observation of rough volatility paths with the good fit of the implied volatility surface to models of fast mean reverting Markov volatilities. Moreover, the result conforms with recent numerical results regarding rough stochastic volatility models. It extends the scope of models for which the asymptotic results of fast mean reverting Markov volatilities are valid. The paper concludes with a general discussion of fractional volatility asymptotics and their interrelation. The regimes discussed there include fast and slow volatility factors with strong or small volatility fluctuations and with the limits not commuting in general. The notion of a characteristic term structure exponent is introduced, this exponent governs the implied volatility term structure in the various asymptotic regimes.     

Regime-switching measure of systemic financial stress

In this paper, I propose an approach to measuring systemic financial stress. In particular, abrupt and large changes in the volatility of financial variables that represent the dynamics of the US financial sector are modeled with a joint regime-switching process, distinguishing “low” and “high” volatility regimes. I find that the joint “high” volatility regime for the TED spread, return on the NYSE index, and capital-weighted CDS spread for large banks is closely related to periods of financial stress. This result suggests that the probability of the joint high volatility regime of these financial variables can be considered as a measure of systemic financial stress.     

Earnings quality and the heterogeneous relation between earnings and stock returns

We adopt a heterogeneous regime switching method to examine the informativeness of accounting earnings for stock returns. We identify two distinct time-series regimes in terms of the relation between earnings and returns. In the low volatility regime (typical of bull markets), earnings are moderately informative for stock returns. But in high volatility market conditions (typical of financial crisis), earnings are strongly related to returns. Our evidence suggests that earnings are more informative to investors when uncertainty and risk is high which is consistent with the idea that during market downturns investors rely more on fundamental information about the firm. Next, we identify groups of firms that follow similar regime dynamics. We find that the importance of accounting earnings for returns in each of the market regimes varies across firms: certain firms spend more time in a regime where their earnings are highly relevant to returns, and other firms spend more time in a regime where earnings are moderately relevant to returns. We also show that firms with poorer accrual quality have a greater probability of belonging to the high volatility regime.     

The frequency of regime switching in financial market volatility

The mechanism of risk responses to market shocks is considered as stagnant in recent financial literature, whether during normal or stress periods. Since the returns are heteroskedastic, a little consideration was given to volatility structural breaks and diverse states. In this study, we conduct extensive simulations to prove that the switching regime GARCH model, under the highly flexible skewed generalized t (SGT) distribution, is remarkably efficient in detecting different volatility states. Next, we examine the switching regime in the S&P 500 volatility for weekly, daily, 10-minute and 1-minute returns. Results show that the volatility switches regimes frequently, and differences between the distributions of the high and low volatility states become more accentuated as the frequency increases. Moreover, the SGT is highly preferable to the usually employed skewed t distribution.     

Modeling Time-Varying Correlations in Volatility Between BRICS and Commodity Markets

This article investigates the asymmetric and long memory volatility properties and dynamic conditional correlations (DCCs) between Brazilian, Russian, Indian, Chinese, and South African (BRICS) stock markets and commodity (gold and oil) futures markets, using the trivariate DCC-fractionally integrated asymmetric power autoregressive conditional heteroskedasticity (FIAPARCH) model. We identify significant asymmetric and long memory volatility properties and DCCs for pairs of BRICS stock and commodity markets, and variability in DCCs and Markov Switching regimes during economic and financial crises. Finally, we analyze optimal portfolio weights and time-varying hedge ratios, demonstrating the importance of overweighting optimal portfolios between BRICS stock and commodity assets.     

How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

Abstract:  We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.     

Smooth transition patterns in the realized stock–bond correlation

This paper explores the time variation in the stock–bond correlation using high-frequency data. Gradual transitions between regimes of negative and positive stock–bond correlation are well accommodated by the smooth transition regression (STR) model. We find that the regimes are systematically related to movements in financial and to a minor extent macroeconomic transition variables. In particular, the most informative transition variables are the short rate, the yield spread, and the VIX volatility index. Importantly, both in-sample and out-of-sample evaluation criteria show that multiple transition variable STR specifications considerably outperform single transition variable STR models. Our results are robust to different forecast horizons.     

The January effect across volatility regimes

Using a Markov regime switching model, this article presents evidence of the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes: one with high volatility and another with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high-volatility regime. In sharp contrast with most of the previous literature, we find two major results: (1) the January effect exists for all sizes of portfolio; (2) the negative correlation between the magnitude of the January effect and portfolio size fails across volatility regimes. Moreover, our evidence supports a slight decline in the January effect for all sizes of portfolio except the smallest, for which it is even larger.     

The restoration of the gold standard after the US Civil War: A volatility analysis

This paper presents a new view on the gold price of greenbacks during and after the American Civil War by analyzing exchange-rate volatility rather than exchange-rate levels. Our empirical investigation detects regimes of high and low volatility alternating in a way that is consistent with a theoretical exchange-rate model in which the rate is primarily driven by investors’ expectations and not by fundamentals. We interpret these findings as evidence that monetary policy makers were surprisingly able to credibly announce the resumption to gold half a year before it actually took place on January 1, 1879. Given the intense political debate about the appropriate design of the United States’ financial system, this is a remarkable result. It indicates that the policy makers’ ability to anchor investors’ expectations is relevant to achieving asset-price stability as well as effectiveness of financial market regulation. The insights from this historical episode should therefore be of interest to policy makers and regulators combating financial crises like the ongoing current debt crises worldwide.     

Is the realized volatility good for option pricing during the recent financial crisis?

The contributions of this paper are threefold. The first contribution is the proposed logarithmic HAR (log-HAR) option-pricing model, which is more convenient compared with other option pricing models associated with realized volatility in terms of simpler estimation procedure. The second contribution is the test of the empirical implications of heterogeneous autoregressive model of the realized volatility (HAR)-type models in the S&P 500 index options market with comparison of the non-linear asymmetric GARCH option-pricing model, which is the best model in pricing options among generalized autoregressive conditional heteroskedastic-type models. The third contribution is the empirical analysis based on options traded from July 3, 2007 to December 31, 2008, a period covering a recent financial crisis. Overall, the HAR-type models successfully predict out-of-sample option prices because they are based on realized volatilities, which are closer to the expected volatility in financial markets. However, mixed results exist between the log-HAR and the heterogeneous auto-regressive gamma models in pricing options because the former is better than the latter in times of turmoil, whereas it is worse during the rather stable periods.     

A nesting framework for Markov-switching GARCH modelling with an application to the German stock market

In this paper, we establish a generalized two-regime Markov-switching GARCH model which enables us to specify complex (symmetric and asymmetric) GARCH equations that may differ considerably in their functional forms across the two Markov regimes. We show how previously proposed collapsing procedures for the Markov-switching GARCH model can be extended to estimate our general specification by means of classical maximum-likelihood methods. We estimate several variants of the generalized Markov-switching GARCH model using daily excess returns of the German stock market index DAX sampled during the last decade. Our empirical study has two major findings. First, our generalized model outperforms all nested specifications in terms of (a) statistical fit (when model selection is based on likelihood ratio tests) and (b) out-of-sample volatility forecasting performance. Second, we find significant Markov-switching structures in German stock market data, with substantially differing volatility equations across the regimes.     

Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection

We consider a GARCH-MIDAS model with short-term and long-term volatility components, in which the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, three variables (namely, housing starts, default spread and realized volatility) are selected from a large set of macroeconomic and financial variables. The recursive out-of-sample forecasting evaluation shows that variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.     

The Volatility of Bid‐Ask Spreads

This study tests whether the volatility of bid‐ask spreads is positively related to expected returns. After controlling for market‐risk factors, we find that the average risk‐adjusted excess return for stocks in the highest spread volatility quintile is around 50 basis points per month. In a variety of multivariate tests, we find robust evidence of a return premium associated with spread volatility that is both statistically significant and economically meaningful. Our results are robust to controls for a variety of stock characteristics, different tick‐size regimes, and other measures of liquidity volatility.     

Scheduled macro-news effects on a Euro/US dollar limit order book around the 2008 financial crisis

Characteristics of a complete limit order book (LOB) for Euro/US dollar in 2006-09, are asymmetrically affected by scheduled macro news announcements during the financial crisis. Depth is the most responsive characteristic followed by spread, volatility and slope. Depth and volatility respond more to expansion surprises, while spread and slope are more sensitive to recession. The effect of the announcement’s occurrence without surprise is overwhelmingly positive (negative) for depth and volatility (spread) in both regimes. This effect is mitigated by the surprise. More than half of US scheduled news surprises have state dependent depth coefficients, most with opposing signs between recession and expansion. Using all quote levels generates stronger characteristic response, indicating the existence of information outside of the best quotes.     

金融资产综合风险价值动态评估研究

为综合度量金融资产损失的市场风险与流动性风险,采用GARCH-VaR模型度量了日市场风险价值,用日内相对波动幅度调整为日LA-VaR,并利用时间延展槡T规则将它转换为变现期间的综合风险价值,构建了金融资产综合风险价值的全方位动态评估模型。通过以中国股指期货为例的实证研究证明,该模型能够有效评估金融资产综合风险价值,适用于金融资产公允价值的期末估算。     

Generative Bayesian neural network model for risk-neutral pricing of American index options

Financial models with stochastic volatility or jumps play a critical role as alternative option pricing models for the classical Black–Scholes model, which have the ability to fit different market volatility structures. Recently, machine learning models have elicited considerable attention from researchers because of their improved prediction accuracy in pricing financial derivatives. We propose a generative Bayesian learning model that incorporates a prior reflecting a risk-neutral pricing structure to provide fair prices for the deep ITM and the deep OTM options that are rarely traded. We conduct a comprehensive empirical study to compare classical financial option models with machine learning models in terms of model estimation and prediction using S&P 100 American put options from 2003 to 2012. Results indicate that machine learning models demonstrate better prediction performance than the classical financial option models. Especially, we observe that the generative Bayesian neural network model demonstrates the best overall prediction performance.