Exploring the predictability of range‐based volatility estimators using recurrent neural networks

We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only.     

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

We use several U.S. and euro‐area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time‐varying uncertainty. We obtain survey‐consistent distributions of future inflation at any horizon, both in the U.S. and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the United States, while mild de‐anchoring occurred in the euro area. As of our sample end, both areas appear to be almost equally anchored.     

Conditional higher order moments in metal asset returns

This study examines the role of higher order moments in the returns of four important metals, aluminium, copper, gold and silver, using the asymmetric GARCH (AGARCH) model with a conditional skewed generalized-t (SGT) distribution. Implications of higher order moments in metal returns are evaluated by comparing the performances of conditional value-at-risk measures obtained from the AGARCH models with SGT distributions to those obtained from the AGARCH models with normal and student-t distributions. With the exception of gold, the AGARCH model with the SGT distribution appears to have the best fit for all metals examined.     

Valuation of forward start options under affine jump-diffusion models

Under the general affine jump-diffusion framework of Duffie et al. [Econometrica, 2000, 68, 1343–1376], this paper proposes an alternative pricing methodology for European-style forward start options that does not require any parallel optimization routine to ensure square integrability. Therefore, the proposed methodology is shown to possess a better accuracy–efficiency trade-off than the usual and more general approach initiated by Hong [Forward Smile and Derivative Pricing. Working paper, UBS, 2004] that is based on the knowledge of the forward characteristic function. Explicit pricing solutions are also offered under the nested jump-diffusion setting proposed by Bakshi et al. [J. Finance, 1997, 52, 2003–2049], which accommodates stochastic volatility and stochastic interest rates, and different integration schemes are numerically tested.     

The role of political uncertainty in Australian financial markets

Within the developed world, recent Australian political history is uniquely turbulent. This situation invokes indecision regarding investment decisions in both the real economy and the financial markets. This paper explores the relationship between uncertainty in Australian federal election polling and resulting financial market uncertainty. Empirical evidence suggests that increasing (decreasing) levels of uncertainty around the election result induce higher (lower) levels of uncertainty in financial markets. The effect is more pronounced as polling day approaches. Industry‐level analysis suggests that the base materials sector is most significantly affected by election uncertainty in Australia.     

Finance and consumption volatility: Evidence from India

The main objective of this paper is to explore the determinants of private consumption growth volatility in India, focusing on the role of financial sector policies. Using data for India over the period 1950-2005, the results show that the implementation of financial repressionist policies is strongly associated with lower consumption volatility. The results remain robust after controlling for a wide range of macroeconomic shocks and variables. The presence of a threshold effect implies that the benefits of financial reforms in reducing consumption volatility can only be reaped when the financial system becomes sufficiently liberalized. The results also indicate that the presence of a more open financial system may serve to dampen fluctuations in private consumption.     

房产税税率、房产税收入与房地产税制度设计

基于房产税税率对房产税收入影响理论,运用数理模型,依据沪渝两市自然数据,考量房产税税率变化对房产税收入的影响.结果显示:房产税税率对房产税收入具有重要影响.若提高上海市房产税税率,房产税增收效果更明显;若下调重庆市房产税税率,房产税增收效应更显著.鉴于此,未来房地产税立法改革思路:一是坚持房地产税税负不增加;二是平衡好房地产税立法与其他收费制度的关系;三是统筹税制改革的协同推进;四是兼顾房地产税的地方财政属性;五是谨慎推进.同时,应遵循税收法定原则、地方财政原则、公平原则、确实原则、渐进原则和结构性调整原则.此外,在制度设计方面,纳税人的设计要体现财产税性质,征税范围要涵盖农村,课税对象要包括存量住房,税率要体现差异性,计税依据要由市场价格评估值确定,税收优惠要兼顾免税面积和特殊人群.     

The maturity effect of stock index futures: Speculation or carry arbitrage?

Using the implementation of trading restrictions on CSI 300 index futures market as a quasi-natural experiment, this paper examines the maturity effect of stock index futures and its determinants. The results show that the maturity effect changes from weakly positive to significantly negative after trading restrictions are implemented. We find that the change in the maturity effect is rooted in the speculative effect, which is measured by the time pattern of price sensitivity to information, while there is a lack of support for the carry arbitrage effect on the maturity effect of index futures. Our findings provide an opportunity to better understand volatility dynamics in the equity futures market.     

Stochastic volatility: A tale of co-jumps,non-normality,GMM and high frequency data

In this article we introduce a linear–quadratic volatility model with co-jumps and show how to calibrate this model to a rich dataset. We apply GMM and more specifically match the moments of realized power and multi-power variations, which are obtained from high-frequency stock market data. Our model incorporates two salient features: the setting of simultaneous jumps in both return process and volatility process and the superposition structure of a continuous linear–quadratic volatility process and a Lévy-driven Ornstein–Uhlenbeck process. We compare the quality of fit for several models, and show that our model outperforms the conventional jump diffusion or Bates model. Besides that, we find evidence that the jump sizes are not normally distributed and that our model performs best when the distribution of jump-sizes is only specified through certain (co-) moment conditions. Monte Carlo experiments are employed to confirm this.     

Time-series and cross-sectional momentum in anomaly returns

We find strong evidence of time-series and cross-sectional momentum in the long–short returns of a comprehensive sample of anomalies. Strategies that exploit such persistence deliver significant abnormal returns that are robust to the stock momentum effect, cannot be explained by traditional asset-pricing models, and are more pronounced when arbitrage capital is scarcer or market liquidity is lower. Momentum in anomaly returns dissipates but does not reverse, in the long-run. Our findings are consistent with limits-to-arbitrage and slow-moving capital causing mispricing to persist. Supporting this explanation, we find that both the level and persistence of anomaly returns are positively related to idiosyncratic volatility.