Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

The future of the dollar–euro exchange rate

Monetary unification in Europe is expected to produce a major new international currency, which may compete with the U.S. dollar as the currency of choice in foreign exchange transactions, financial asset markets and central bank reserves. This study considers two important issues regarding the euro: its global role as medium of exchange, unit of account, and store of value and its position relative to the U.S. dollar. Among the main considerations are differences in cyclical behavior, inflation differentials, trade patterns and capital flows, and risk-return assessments. External diversification of private portfolios and of central banks’ reserve holdings will play a key role in determining the euro’s exchange rate. Overall, despite its rough start, we argue that the euro may emerge as a challenger to the U.S. dollar.     

Forecasting stock market volatility: Can the risk aversion measure exert an important role?

In this paper, we predict realized volatility of stock return by utilizing time-varying risk aversion based on a simple linear autoregressive model. Our in-sample results suggest that time-varying risk aversion have significant impact for stock return volatility. In terms of out-of-sample forecasting performance, the empirical results indicate that the incorporation of time-varying risk aversion in the benchmark model can yield more accurate stock return volatility forecasts. Notably, the out-of-sample forecasting results confirm that our conclusions are robust when we apply alternative lag orders and alternative prediction evaluation periods. Finally, we study links between the prediction ability of time-varying risk aversion and the volatility of other stock indices and two kinds of crude oil, and find that the new predictor can effectively strengthen forecasting performance in most case. In view of the importance of volatility risk in the asset pricing process, our research is of great significance for financial asset participants.     

Does financial integration affect real exchange rate volatility and cross-country equity market returns correlation?

Existing empirical studies show that financial integration affects the behavior of average excess returns, cross-country equity market returns (EMR) correlations and real exchange rate (RER) volatility. We employ a recently developed two-country model with recursive preferences, frictionless and complete markets and highly correlated long-run innovations to examine whether full financial integration (i.e. full risk-sharing) affects the US-Canada EMR correlation and the US RER volatility, consistently with existing empirical findings. First, full risk-sharing gives rise to a relatively high RER volatility. Second, it induces very strong positive cross-country EMR correlations. Both quantities are higher than those observed in the US-Canada asset pricing data, and increase as the risk-sharing incentive increases. In contrast, “international consumption quantities” are weakly sensitive to changes in the level of aversion to consumption and utility risk.     

Is the first UK rate hike imminent?

• The events of the past couple of months have led us to push out our call for the first UK rate hike from February 2016 to May 2016. The financial market turmoil of late summer, increasingly dovish commentary from the MPC, the enduring strength of sterling and the likelihood of lower inflation over the next six months are all factors which point to the first rate hike coming later.
• This is a modal forecast which is consistent with our baseline forecast playing out. But the escalating downside risks would imply a mean forecast which shows the first rate hike coming even later still and the subsequent path of monetary tightening being shallower. This explains why the expectations of financial markets may appear to be more bearish than our own.

     

Do high-frequency measures of volatility improve forecasts of return distributions?

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV)$log\left(RV\right)$ dynamics; the timing of information availability; and the assumed distributions of both return and log(RV)$log\left(RV\right)$ innovations. We find that a joint model of returns and volatility that features two components for log(RV)$log\left(RV\right)$ provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns.     

Is the service sector different in size heterogeneity?

We examined whether significant differences in size heterogeneity exist between the service and the manufacturing industries by using PL exponents as the proxy for intra-industry size heterogeneity. For the purpose, we analyzed firm size distribution (FSD) and estimated the PL exponents, on the right tails of FSD, of the service and manufacturing industries in Korea for the period 2008–2012 using the Business Activity Survey dataset created by the Korean National Statistical Office As a result, we observed that the estimates of the PL exponents for the service industry are lower than those for the manufacturing industry (\(\upalpha _\mathrm{Service}<\upalpha _\mathrm{Manufacturing}\)) regardless of size variable, year, and dataset. This relationship may be related to the weaker negative relationship between the size and growth of the service industry, which made the slope of the PL distribution in the right tail of the FSD smoother. This finding implies that size heterogeneity may be more distinctive in the service industry than in the manufacturing industry. In addition, the PL exponents of sales were larger than those of assets and smaller than those of employees (\(\upalpha _\mathrm{Asset}<\upalpha _\mathrm{Sales}<\upalpha _\mathrm{Employee}\)) regardless of industry, year, and dataset. We also observed the PL exponents in the survived-firm dataset to decrease, compared to those in the all-firm dataset.     

Adopt the euro? The GME approach

The objective of this paper is to evaluate the degree of financial integration achieved in the European Union based on covered interest parity and using Generalized Maximum Entropy. EU countries are divided into two groups according to their current situation with respect to the adoption of the euro. Financial integration before the adoption of the euro is analyzed for the countries that adopted the euro in 1999. Similarly, current financial integration is evaluated for non-euro EU countries. Besides the importance of comparing the situation of the non-euro EU countries with the situation of the euro EU countries previous to the euro adoption, which may be useful to evaluate an eventual decision of the non-euro members to adopt the euro, it is interesting to analyze the performance of Generalized Maximum Entropy. Generalized Maximum Entropy has the ability to estimate the parameters of a regression model without imposing any constrains on the probability distribution of errors and it is robust even when we have ill-posed problems. Overall our results suggest that the degree of financial integration on non-euro countries is lower than the degree of financial integration that existed among euro adopting countries before the adoption of the euro.     

The interest rate spreads in the Czech Republic: Different loans,different determinants?

We analyze the determinants of interest rate spreads of different loan categories in the Czech Republic during 2004–2011. We employ a detailed bank supervisory dataset that allows us to construct the actual spreads for four loan categories, namely small and large corporate loans, consumer loans and mortgages, on a monthly basis. Our regression analysis shows that bank and macroeconomic characteristics matter more for setting the spreads for small corporate loans and mortgages rather than for large corporate loans and consumer loans. Interest rate risk determines the spreads for all loan categories. The global financial crisis has, to a certain extent, increased the responsiveness of spreads to interest rate risk and liquidity risk.     

Brighter prospects? Assessing the franchise advantage using census data

This paper uses Census microdata to examine how starting a business as a franchise rather than an independent business affects its survival and growth prospects. We assess factors that influence the decision to become a franchisee and use various empirical approaches to correct for selection bias in our performance analyses. We find that franchised businesses on average exhibit higher survival rates than independent businesses; but importantly, the difference is small compared with claims in the trade press. The effect is also short lived: conditional on surviving a year or two, we no longer find survival (or growth) differences. We then explore two potential sources for this small survival advantage, namely franchisors’ screening process and the benefits arising from the brand and business know‐how provided by franchisors. We find evidence that both of the sources contribute to the franchising advantage.     

Is there a unit root in the inflation rate? Evidence from sequential break and panel data models

Using sequential trend break and panel data models, we investigate the unit root hypothesis for the inflation rates of thirteen OECD countries. With individual country tests, we find evidence of stationarity in only four of the thirteen countries. The results are more striking with the panel data model. We can strongly reject the unit root hypothesis both for a panel of all thirteen countries and for a number of smaller panels consisting of as few as three countries. The non-rejection of the unit root hypothesis for inflation is very fragile to even a small amount of cross-section variation. © 1997 John Wiley & Sons, Ltd.     

Do terror attacks affect the dollar-pound exchange rate? A nonparametric causality-in-quantiles analysis

While much significant research has been done to study the effects of terror attacks on stock markets, less is known about the response of exchange rates to terror attacks. We suggest a non-parametric causality-in-quantiles test to study whether (relative) terror attacks affect exchange-rate returns and volatility. Using data on the dollar-pound exchange rate to illustrate the test, we show that terror attacks mainly affect the lower and upper quantiles of the conditional distribution of exchange-rate returns, while misspecified (due to nonlinearity and structural breaks) linear Granger causality test show no evidence of predictability. Terror attacks also affect almost all quantiles of the conditional distribution of exchange-rate volatility (except the extreme upper-end), with the significance of the effect being particularly strong for the lower quantiles. The importance of terror attacks is shown to hold also under an alternative measure of volatility and for an important emerging-market exchange rate as well.     

Is small beautiful? Size effects of volatility spillovers for firm performance and exchange rates in tourism

This paper examines the size effects of volatility spillovers for firm performance and exchange rates with asymmetry in the Taiwan tourism industry. The analysis is based on two conditional multivariate models, BEKK–AGARCH and VARMA–AGARCH, in the volatility specification. Daily data from 1 July 2008 to 29 June 2012 for 999 firms are used, which covers the Global Financial Crisis. The empirical findings indicate that there are size effects on volatility spillovers from the exchange rate to firm performance. Specifically, the risk for firm size has different effects from the three leading tourism sources to Taiwan, namely USA, Japan, and China. Furthermore, all the return series reveal quite high volatility spillovers (at over 60%) with a one-period lag. The empirical results show a negative correlation between exchange rate returns and stock returns. However, the asymmetric effect of the shock is ambiguous, owing to conflicts in the significance and signs of the asymmetry effect in the two estimated multivariate GARCH models. The empirical findings provide financial managers with a better understanding of how firm size is related to financial performance, risk and portfolio management strategies that can be used in practice.     

Sources of asymmetric shocks: The exchange rate or other culprits?

We analyze and quantify the determinants of medium-term real exchange rate (RER) changes. First, we discuss the sources of asymmetric shocks causing exchange rate variability and the role of the RER as a shock absorber or generator. Secondly, we use data for 21 advanced and late transition economies to gauge the extent to which medium-term bilateral real exchange rate variability can be explained by various fundamental factors. Using Bayesian model averaging, we find that out of 22 factors under consideration, four types of dissimilarities within a given pair of economies are likely to be included in the true model: dissimilarities as regards (i) financial development, (ii) per capita income growth, (iii) central bank autonomy, and (iv) the structure of the economy. A regression based on these four factors indicates that they explain about 96 percent of the sample average level of three year RER variability. In the logic of our approach, the remaining part of the total variability represents an upper estimate of the influence of the foreign exchange market itself. For our sample, the contribution of the real exchange rate itself to asymmetric shocks therefore appears to be very low.     

Which implied volatility provides the best measure of future volatility?

The volatility smile/skew phenomenon makes it unclear which implied volatility provides the best measure of the market volatility expectation over the remaining life of the option. Due to the high liquidity of at-the-money option and the low sensitivity of its implied volatility to the price error, the at-the-money implied volatility is often considered a good measure of future volatility. In this paper, we raise the question: is at-the-money implied volatility the best we can do? We provide in this paper an analytical rationale that the implied volatility from option with highest vega outperforms the at-the-money implied volatility in terms of forecasting ability, especially for long forecasting horizons. Our empirical findings are consistent with our theoretical argument.     

Forecasting the M4 competition weekly data: Forecast Pro’s winning approach

Forecast Pro forecasted the weekly series in the M4 competition more accurately than all other entrants. Our approach was to follow the same forecasting process that we recommend to our users. This approach involves determining the Key Performance Metric (KPI), establishing baseline forecasts using our automated expert selection algorithm, reviewing those baseline forecasts and customizing forecasts where needed. This article explores why this approach worked well for weekly data, discusses the applicability of the M4 competition to business forecasting and proposes some potential improvements for future competitions to make them more relevant to business forecasting.     

Exchange market pressure on the pound–dollar exchange rate: 1925–1931

This is a study of exchange market pressure against the pound sterling during the inter-war period. The main findings are that (a) the behavior of U.K. fundamentals relative to those of the U.S.A. helps to explain exchange market pressure against the pound; (b) during the run-up to devaluation in September 1931 the monetary authorities in the U.K. were acting to reduce domestic credit; but (c) additional pressure was brought against the pound from speculative sources. These findings relate to current thinking on the choice of exchange rate regime as even well-behaved fundamentals may not be sufficient to sustain a currency on its peg.     

Is newer always better? A reinvestigation of productivity dynamics using updated PWT data

Journal of Productivity Analysis - Understanding the drivers of productivity remains one of the most sought after phenomena in economics. The ability to create produce more from less resources is...     

Monetary/asset models of exchange rate determination: How well have they performed in the 1980's?

This study compares the performance of various structural exchange rate models based on the methodology developed by Meese and Rogoff (1983 a,b, 1985). means of improving forecast performance.