Modelling the implied volatility surface based on Shanghai 50ETF options

We develop a dynamic factor model to forecast the implied volatility surface (IVS) of Shanghai Stock Exchange 50ETF options. Based on the assumption that dynamic change in IVS is mean-reverting and Markovian, we use a state space model to capture the dynamics of IVS, and set the latent factors to be the Ornstein–Uhlenbeck processes. We obtain the optimal estimations of parameters using the Kalman filter algorithm. Empirical results show that our model performs better than the traditional IVS model in terms of fitting ability and prediction performance.     

Fear connectedness among asset classes

This study investigates the interconnection between five implied volatility indices representative of different financial markets during the period 1 August 2008–29 December 2017. To this end, we first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period (the system-wide approach) using a framework recently proposed by Diebold and Yilmaz. Second, we make use of a dynamic analysis to evaluate both the net directional connectedness for each market and all net pairwise directional connectedness. Our results suggest that a 38.99%, of the total variance of the forecast errors is explained by shocks across markets, indicating that the remainder 61.01% of the variation is due to idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability. Finally, we also document frequently switch between a net volatility transmitter and a net volatility receiver role in the five markets under study.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

On the stochastic elasticity of variance diffusions

The elasticity of variance of risky assets has been observed to be rapidly fluctuating around a level. The level itself slowly varies depending upon the corresponding economic situation at the time of consideration. In particular, it turns out to be extraordinary during the peak period of the 2007–2009 Global Financial Crisis. Based on the concept of stochastic elasticity of variance, this paper develops an asset price model in a multiscale form and applies it to the pricing of European options and verifies a significant improvement over the constant elasticity of variance model in terms of the geometric structure (skew or smirk) of implied volatility. Our result implies that a theoretical model based on the random elasticity can derive market's volatility forecast more accurately than the constant elasticity so that investors can employ a dynamic investment strategy reducing risk more effectively.     

Detecting and Predicting Forecast Breakdowns

We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss function, is significantly worse than its in-sample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data-generating process and relate the properties of our forecast breakdown test to those of structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips' curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed.     

Stochastic Models of Implied Volatility Surfaces

We propose a market–based approach to the modelling of implied volatility, in which the implied volatility surface is directly used as the state variable to describe the joint evolution of market prices of options and their underlying asset. We model the evolution of an implied volatility surface by representing it as a randomly fluctuating surface driven by a finite number of orthogonal random factors. Our approach is based on a Karhunen–Loeve decomposition of the daily variations of implied volatilities obtained from market data on SP500 and DAX options.
We illustrate how this approach extends and improves the accuracy of the well–known 'sticky moneyness' rule used by option traders for updating implied volatilities. Our approach gives a justification for the use of 'Vegas' for measuring volatility risk and provides a decomposition of volatility risk as a sum of independent contributions from empirically identifiable factors.
(J.E.L.: G130, C14, C31).     

Hedging and diversification across commodity assets

ABSTRACT

We investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.     

Fitting survey expectations and uncertainty about trend inflation

Many studies document that the inflation rate is governed by persistent trend shifts and time-varying uncertainty about trend inflation. As both these quantities are unobserved, a forecaster has to learn about changes in trend inflation by a signal extraction procedure. I suggest that the forecaster uses a simple IMA(1, 1) model because it is well suited to forecast inflation and it provides an efficient way to solve the signal extraction problem. I test whether this model provides a good fit for expectations from the Survey of Professional Forecasters. The model appears to be well suited to model observed inflation expectations if we allow for stochastic volatility. When I estimate the implied learning rule, results are supportive for the trend learning hypothesis. Moreover, stochastic volatility seems to influence the way agents learn over time. It appears that survey participants systematically adapt their learning behavior when inflation uncertainty changes.     

A range-based volatility approach to measuring volatility contagion in securitized real estate markets

We use a newly-developed time-varying range-based volatility model to capture the dynamics of securitized real estate volatility. The novelty of the model is the use of a smooth transition copula function to capture the nonlinear comovements between major REIT markets in the presence of structural changes. We then investigate the impact of extreme events on the volatility dependence in a broad set of 13 developed countries over the period from 1990 to 2012. We find that information transmission through the volatility channel can exhibit either bi- or uni-directional causality. In addition, financial contagion following the subprime crisis is found between the U.S. and Australia.     

Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility

We show that historical volatility from high frequency returns outperforms implied volatility when standardized returns by historical volatility tends to be normally distributed. For the FTSE 100 futures, we find that historical volatility using high frequency returns outperforms implied volatility in forecasting future volatility. However, we find that implied volatility outperforms historical volatility in forecasting future volatility for the S&P 500 futures. The results also indicate that historical volatility using high frequency returns could be an unbiased forecast for the FTSE 100 futures.     

Revisiting the long memory dynamics of the implied–realized volatility relationship: New evidence from the wavelet regression

The literature studying stock index options confirms severe biases and inefficiencies in using implied volatility as a forecast of future volatility. In this paper, we revisit the implied–realized volatility relationship with wavelet band least squares (WBLS) exploring the long memory of volatility, a possible cause of the bias. Using the S&P 500 and DAX monthly and bi-weekly option prices covering the recent financial crisis, we conclude that the implied–realized volatility relation is driven solely by the lower frequencies of the spectra representing long investment horizons. The findings enable improvement of future volatility forecasts as they support unbiasedness of implied volatility as a good proxy for future volatility in the long run.     

Regime-switching purchasing power parity in Latin America: Monte Carlo unit root tests with dynamic conditional score

We suggest a Monte Carlo simulation-based unit root test of the purchasing power parity theory for Latin American countries. Under the null hypothesis, we use a Markov regime-switching (MS) model with unit root in the conditional location and MS volatility dynamics. Under the alternative hypothesis, the proposed test incorporates Markov regime-switching autoregressive moving average (MS-ARMA) plus MS volatility dynamics. Under both the null and alternative hypotheses, one of the volatility models estimated is Beta-t-EGARCH, which is a recent dynamic conditional score volatility model. We use data on real effective exchange rate time series for 14 Latin American countries. For each country, we estimate by Monte Carlo simulation the critical values of the unit root test. We provide an economic discussion of the unit root test results and also study the robustness of MS-ARMA plus MS volatility with respect to smooth transition autoregressive models with Fourier function.     

Options-based forecasts of futures prices in the presence of limit moves

The reported analysis examines a simultaneous estimation option-based approach to forecast futures prices in the presence of daily price limit moves. The procedure explicitly allows for changing implied volatilities by estimating the implied futures price and the implied volatility simultaneously. Using futures and futures options data for three agricultural commodities, it is found that the simultaneous estimation approach accounts for the abrupt changes in implied volatility associated with limit moves and generates more accurate price forecasts than conventional methods that rely on only one implied variable.     

S&P500 volatility analysis using high-frequency multipower variation volatility proxies

The availability of ultra-high-frequency data has sparked enormous parametric and nonparametric volatility estimators in financial time series analysis. However, some high-frequency volatility estimators are suffering from biasness issues due to the abrupt jumps and microstructure effect that often observed in nowadays global financial markets. Hence, we motivate our studies with two long-memory time series models using various high-frequency multipower variation volatility proxies. The forecast evaluations are illustrated using the S&P500 data over the period from year 2008 to 2013. Our empirical studies found that higher-power variation volatility proxies provide better in-sample and out-of-sample performances as compared to the widely used realized volatility and fractionally integrated ARCH models. Finally, these empirical findings are used to estimate the one-day-ahead value-at-risk of S&P500.     

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.     

House price dynamics,conditional higher-order moments,and density forecasts

Most studies on housing price dynamics are only concerned with the conditional mean and variance, but overlook other higher-order conditional moments and the structural change characteristics inherent in housing prices. In order to take into account these two important issues, this study utilizes the generalized Markov switching GARCH model to explore house price dynamics and conditional distribution for US market over 1975Q1–2007Q4. The housing return follows two distinct dynamics: the bust regime and the boom regime. The volatility pattern is different in the bust and boom regimes. In addition, the conditional densities derived by the regime-switching model change dramatically over time and are significantly different from normal distribution. More importantly, the regime-switching model can detect in advance a weak US housing market such as the one that occurred in the middle of 2007. The in-sample fitting ability of regime-switching model, which incorporates higher-order moments, has significant improvements compared to the single-regime AR and AR-GARCH models. For the out-of-sample Value-at-Risk forecasting performance, the ability of regime-switching AR-GARCH model to forecast one-step-ahead density is better compared to the single-regime AR-GARCH model.     

FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.     

The inflation and inflation uncertainty relationship for Turkey: a dynamic framework

This article assesses the interaction between inflation and inflation uncertainty in a dynamic framework for Turkey by using monthly data for the time period 1984–2009. The bulk of previous studies investigating the link between inflation and inflation uncertainty employ Autoregressive Conditional Heteroskedasticity (ARCH)-type models, which consider inflation uncertainty as a predetermined function of innovations to inflation specification. The stochastic volatility in mean (SVM) models that we use allow for gathering innovations to inflation uncertainty and assess the effect of inflation volatility shocks on inflation over time. When we assess the interaction between inflation and its volatility, the empirical findings indicate that response of inflation to inflation volatility is positive and statistically significant. However, the response of inflation volatility to inflation is negative but not statistically significant.     

An environmental assessment of future production-related technological change: 1970–2000 (An input-output approach)

This paper develops a technique, or model, to systematically assess the environmental impact of specific technological changes forecast to occur over this and the next two decades. The core of the model is a dynamic technical coefficient matrix of a large input-output model. The technological change considered is that which affects the coefficients of this matrix and thus the distribution of material inputs over time into the various sectors of the U.S. economy. An environmental assessment of this production-related technological change is achieved through a submodel that registers production residuals on an industry basis for 14 waste categories.     

An Empirical Examination of the Relationship Between Central Bank Intervention and Exchange Rate Volatility: Some Australian Evidence

Arguably, market stability is one of the primary reasons behind government intervention in the foreign exchange market. Whether or not the authorities achieve this goal is an empirical matter and testing of this issue is made difficult by the fact that government intervention and exchange rate volatility may be jointly determined. In this paper, the extent to which volatility drives intervention is considered using PROBIT analysis. The results suggest that while support for the hypothesis exists, volatility on its own does not to provide enough information to allow us to accurately forecast government intervention. A modified GARCH model is then tested which incorporates the impact of government intervention in the mean and conditional variance equation. The evidence presented suggest that the dynamics of market are different on the days where the central bank is active in the market.