Forecasting exchange rate volatility using conditional variance models selected by information criteria

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used GARCH(1, 1) model. An examination of the orders of models selected by the criteria reveals that (1, 1) models are typically selected less than 20% of the time.

Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

Idiosyncratic volatility and the pricing of poorly-diversified portfolios

This article examines the role of idiosyncratic volatility in explaining the cross-sectional variation of size- and value-sorted portfolio returns. We show that the premium for bearing idiosyncratic volatility varies inversely with the number of stocks included in the portfolios. This conclusion is robust within various multifactor models based on size, value, past performance, liquidity and total volatility and also holds within an ICAPM specification of the risk–return relationship. Our findings thus indicate that investors demand an additional return for bearing the idiosyncratic volatility of poorly-diversified portfolios.

The importance of the volatility risk premium for volatility forecasting

In this paper, we study the role of the volatility risk premium for the forecasting performance of implied volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more than 20 years of options and futures data on three major energy markets. Using regression models and statistical loss functions, we find compelling evidence to suggest that the risk premium adjusted implied volatility significantly outperforms other models, including its unadjusted counterpart. Our main finding holds for different choices of volatility estimators and competing time-series models, underlying the robustness of our results.

Low-frequency volatility of real estate securities in relation to macroeconomic risk

Real estate securities have a number of distinct characteristics that differentiate them from stocks generally. Key amongst them is that under-pinning the firms are both real as well as investment assets. The connections between the underlying macro-economy and listed real estate firms is therefore clearly demonstrated and of heightened importance. To consider the linkages with the underlying macro-economic fundamentals we extract the ‘low-frequency’ volatility component from aggregate volatility shocks in 11 international markets over the 1990-2014 period. This is achieved using Engle and Rangel’s (2008) Spline-Generalized Autoregressive Conditional Heteroskedasticity (Spline-GARCH) model. The estimated low-frequency volatility is then examined together with low-frequency macro data in a fixed-effect pooled regression framework. The analysis reveals that the low-frequency volatility of real estate securities has strong and positive association with most of the macroeconomic risk proxies examined. These include interest rates, inflation, GDP and foreign exchange rates.

The missing pillar: the creativity theory of knowledge spillover entrepreneurship

Knowledge spillover theory of entrepreneurship and the prevailing theory of economic growth treat opportunities as endogenous and generally focus on opportunity recognition by entrepreneurs. New knowledge created endogenously results in knowledge spillovers enabling inventors and entrepreneurs to commercialize it. This article discusses that knowledge spillover entrepreneurship depends not only on ordinary human capital, but more importantly also on creativity embodied in creative individuals and diverse urban environments that attract creative classes. This might result in self-selection of creative individuals into entrepreneurship or enable entrepreneurs to recognize creativity and commercialize it. This creativity theory of knowledge spillover entrepreneurship is tested utilizing data on European cities.

Do jumps matter for volatility forecasting? Evidence from energy markets

This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in-sample and out-of-sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions

Creativity, entrepreneurship and economic development: city-level evidence on creativity spillover of entrepreneurship

We examine the black box of creativity, entrepreneurship and economic development by asking about the mechanisms through which creativity can influence economic development in cities. We propose that, like the knowledge spillover theory of entrepreneurship, creativity spillovers occur and can be slowed by a creativity filter. We examine how creativity and entrepreneurship, and creativity and a melting pot environment, interact to influence urban economic development. Using data on 187 cities in 15 European countries for the period 1999–2009, we advance the extant literature by providing evidence on the existence and dynamics of a creativity filter.

Accounting quality, information risk and implied volatility around earnings announcements

We examine the impact of accounting quality, used as a proxy for information risk, on the behavior of equity implied volatility around quarterly earnings announcements. Using US data during 1996–2010, we observe that lower (higher) accounting quality significantly relates to higher (lower) levels of implied volatility (IV) around announcements. Worse accounting quality is further associated with a significant increase in IV before announcements, and is found to relate to a larger resolution in IV after the announcement has taken place. We interpret our findings as indicative of information risk having a significant impact on implied volatility behavior around earnings announcements.

Using stochastic volatility models to analyse weekly ozone averages in Mexico City

In this paper we make use of some stochastic volatility models to analyse the behaviour of a weekly ozone average measurements series. The models considered here have been used previously in problems related to financial time series. Two models are considered and their parameters are estimated using a Bayesian approach based on Markov chain Monte Carlo (MCMC) methods. Both models are applied to the data provided by the monitoring network of the Metropolitan Area of Mexico City. The selection of the best model for that specific data set is performed using the Deviance Information Criterion and the Conditional Predictive Ordinate method.

Scaling the volatility of credit spreads: evidence from Australian dollar eurobonds

The linear rescaling of the variance of an asset's return is used by many asset pricing models when an annualised risk coefficient is required. However, this approach may not be appropriate for time series, which are not independent and identically distributed (IID). This paper investigates the scaling relationships for daily credit spreads, from January 1995 to May 1998, between AAA-, AA-, and A-rated Australian dollar denominated Eurobonds with maturities of 2, 5, 7, and 10 years. The credit spread return all display similar scaling properties with the estimated standard deviation, based upon a scaling at the square root of time, significantly underestimating the actual level of risk predicted from a normal distribution. These results have implications for risk managers and trading of credit spread instruments.

Structural effects and spillovers in HSIF, HSI and S & P500 volatility

We adopt a BEKK-GARCH framework and employ a systematic approach to jointly examine structural breaks in the Hong Kong cash index and index futures volatility and volatility spillovers from the S&P 500 cash and futures. Multiple switching dummy variables are included in the variance equations to test for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market. Abolishment of the up-tick rule, increase of initial margins and electronic trading of the Hang Seng Index Futures (HSIF) are found to have significant impact when US market spillovers are excluded from a restricted model. Volatility spillovers from the US market are found to have a significant impact and account for some mis-specification in the restricted model.

Regulatory Change v's volume of trade effects in HSIF and HSI volatility

In an earlier paper we adopted a BEKK-GARCH framework and employed a systematic approach to examine structural breaks in the HSIF and HSI volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market. A Bi-variate GARCH model with 3 switching points was found to be superior as it captured the potential structural changes in return volatilities. Abolishment of the uptick rule, increase of initial margins for the HSIF and electronic trading of HSIF were found to have significant impact on the volatility structure of HSIF and HSI. In this paper we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant.

Regulatory change, structural breaks, and transmission effects in HSIF and HSI volatility

A systematic BEKK-GARCH model with multiple switch points in the variance equations captures the structural changes that have taken place in the Hong Kong markets. Abolishment of the uptick rule in the Hong Kong stock market, increase of initial margins, and electronic trading of Hang Seng Index Futures are found to have significant impacts. These changes affect the volatility structure of the HSI and HSIF and hence their lead-lag relationship. The multivariate GARCH model with three specific switching points is found to be superior to any other combination of up to six separate switch points.