The information content of implied volatility in developed versus developing FX markets

This article predicts the daily movement of monthly foreign exchange (FX) rate volatility using a linear combination of a time-series model and implied volatilities from options. The focus is on analysing the FX volatilities in three developing economies (the Brazilian real (BRL), the Indian rupee (INR) and the Russian ruble (RUB)) against the US dollar (USD). The empirical exercise utilizes two time-series models, mixed data sampling (MIDAS) and GARCH. The analysis indicates that for both developed and developing economies the predictive power of MIDAS and that of GARCH is comparable. Further on in this article, we will ascertain whether the relationship between realized and implied volatility is fundamentally different in the case of developing economies from that among developed economies. Thus, we compare the pairs USD/BRL, USD/INR and USD/RUB against EURO/USD and USD/Japanese yen to determine the information content and predictive power of implied volatilities. Plots of the MIDAS coefficients show that the volatility is more persistent in developing economies than in developed economies.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

Calibration of implied volatility for the exchange rate for the Chinese Yuan from its derivatives

Revised implied volatility curves and surfaces for the Chinese Yuan (CNY) exchange rate are obtained from market quotations for CNY non-deliverable options by solving an inverse problem of foreign exchange option pricing, which is calculated using a regularization approach in an optimal control framework. To take account of the market expectation for the CNY exchange rate, a stochastic adjusted factor is applied that follows a Vasicek model with parameters fitted from market quotations for CNY non-deliverable forwards. A well-posed numerical scheme is implemented.     

A new generalized volatility proxy via the stochastic volatility model

This article proposes power transformation of absolute returns as a new proxy of latent volatility in the stochastic model. We generalize absolute returns as a proxy for volatility in that we place no restriction on the power of absolute returns. An empirical investigation on the bias, mean square error and relative bias is carried out for the proposed proxy. Simulation results show that the new estimator exhibiting negligible bias appears to be more efficient than the unbiased estimator with high variance.     

Forecasting the oil futures price volatility: A new approach

This study provides a new perspective of modelling and forecasting realized range-based volatility (RRV) for crude oil futures. We are the first to improve the Heterogeneous Autoregressive model of Realized Range-based Volatility (HAR-RRV) model by considering the significant jump components, signed returns and volatility of realized range-based volatility. The empirical results show that the volatility of volatility significantly exists in the oil futures market. Moreover, our new proposed models with significant jump components, signed returns and volatility of volatility can gain higher forecast accuracy than HAR-RRV-type models. The results are robust to different forecasting windows and forecasting horizons. Our new findings are strategically important for investors making better decisions.     

The Black–Scholes model as a determinant of the implied volatility smile: A simulation study

The paper represents an initial effort to shed light on the determinants of the implied volatility smile in financial (derivative) markets. It fully details the implications of the institutionalization of the Black–Scholes model in an uncertain world populated by individuals who are bounded by the amount of calculation or accounting which is technically possible. Combining model simulations, empirical analysis, and mathematical derivations, the paper proposes that the determinants of the volatility smile might be related to the behavior of traders. In pricing options, they use the widely accepted Black–Scholes formula with a measure of stock volatility that they derive from their subjective beliefs. Moreover, heterogeneity of traders’ beliefs and the way traders update their expectations have nontrivial effects, both on equilibrium prices and on the emergence of the implied volatility smile.     

A new approach to the effect of intervention frequency on the foreign exchange market: evidence from Japan

This article estimates and analyses the effect of intervention frequency on the yen/dollar market, using daily intervention data. We examine using a nonlinear methodology, with the frequency of intervention from April 1991 to December 2005 as a focal explanatory variable. In this article, we also introduce a flexible target zone model that is capable of characterizing the dynamic behaviour of an exchange rate implied by the original target zone model of Krugman and its modifications. The empirical results show the importance of considering the threshold effect when analysing the effect of intervention due to the presence of asymmetry in the foreign exchange market. Moreover, we show that a high frequency intervention stabilizes the exchange rate by reducing exchange rate volatility, especially when the yen appreciates.     

A new energy model to capture the behavior of energy price processes

Owing to the vague fluctuation of energy prices from time to time, a new energy model, which considers both the mean-reverting behavior and the long memory property, is proposed in this paper. Since the problem of estimating parameters, in discrete time for this model, plays a central role in forecast inference, the problem of estimating the unknown parameters has been dealt with for the fractional Ornstein–Uhlenbeck process observed discretely. The asymptotic properties of these estimates are also provided. The numerical simulation results confirm the theoretical analysis and show that our method is effective. To show how to apply our approach in realistic contexts, an empirical study of energy in China, namely Daqing crude oil, is presented. The empirical results seem reasonable when compared to the real data.     

A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)

In this paper, using daily data for six major international stock market indexes and a modified EGARCH specification, the links between stock market returns, volatility and trading volume are investigated in a new nonlinear conditional variance framework with multiple regimes and volume effects. Volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing, seem to demonstrate that the MSV-EGARCH complex threshold structure is able to correctly fit GARCH-type dynamics of the series under study and dominates competing standard asymmetric models in several of the considered stock indexes.

A new methodology for the logistic analysis of evolutionary S-shaped processes: Application to historical time series and forecasting

A new multi-logistic methodology to analyze long range time series of evolutionary S-shaped processes is presented. It conceptually innovates over the traditional logistic approach. The ansatz includes computing the residuals to an optimized multi-logistic trend curve least squares fitted to the time-series data. The elements of the residuals series are checked for autocorrelations and once detected the residuals series is further analyzed to search for eventual presence of underlying periodic structures using a truncated Fourier sine series. The method foundations ensures both a universal applicability and a capacity to disclose the existence of active clocks that can be possibly traced to the driving motors of the evolutionary character of the time series, due to the responsiveness of corresponding process to the development of economic cycles. On associating these two views, it is found that the methodology has a strong potential to improve the quality of short-term forecasts. These findings have been put to test through applications of the methodology to studying the time evolution of two commodities of strong economic and social importance (corn and steel) and good results were consistently obtained for both the analytical and forecasting aspects.