Foreign exchange,fractional cointegration and the implied–realized volatility relation

Almost all relevant literature has characterized implied volatility as a biased predictor of realized volatility. In this paper we provide new time series techniques to investigate the validity of this finding in several foreign exchange options markets, including the Euro market. First, we develop a new fractional cointegration test that is shown to be robust to both stationary and non-stationary regions. Second, we employ both intra-day and daily data to measure realized volatility in order to assess the relevance of data frequency in resolving the bias. Third, we use data on implied volatility traded on the market. In contrast to previous studies, we show that the frequency of data used for measuring realized volatility within a fractionally cointegrating framework is important for the results of unbiasedness tests. Significantly, for many popular exchange rates, the use of intra-day rather than daily data affects the emergence of a different bias, as the possibility of a fractionally integrated risk premium admits itself!     

Using self-organizing maps to adjust for intra-day seasonality

The existence of an intra-day seasonality component in financial market variables (volatility, volume, activity, etc.) has been highlighted in many previous studies. To remove this cyclical component from raw data, many researchers use the intra-day average observations model (IAOM) and/or some smoothing techniques (e.g. the kernel method). When the seasonality is related to the first moment (the conditional expectation) and involves only a deterministic component, the IAOM method succeeds in estimating the periodicity almost perfectly. However, when seasonality affects the first or the second moment (the conditional variance) of the data and contains both deterministic and stochastic components, both IAOM and the kernel method fail to capture it. We introduce self-organizing maps (SOM) as a solution. SOM are based on neural network learning and nonlinear projections. Their flexibility allows seasonality to be captured even in the presence of stochastic cycles.     

Impact of macro-economic surprises on carry trade activity

Can official news and policy announcements affect foreign exchange speculation? A widespread speculative strategy in foreign exchange markets is carry trade. This paper explores the links between macro-economic news and foreign exchange options to identify macro-economic fundamentals most relevant to the pricing of downside risk – measured by risk reversals options contracts – to carry trade activity. Focusing primarily on the Japanese yen carry trade, we identify a significant impact of macro-economic surprises on dollar/yen risk reversals. The effect is sizeable, with news related to bilateral trade balance of particular concern. Moreover, there is a close link between risk reversals and speculative futures positions in Japanese yen. This allows us to quantify a substantial effect of macro-economic news on carry trade activity, with the cost of hedging as the transmission mechanism.     

Does the forward premium puzzle disappear over the horizon?

This paper provides the first comprehensive study of the horizon effect in tests of the forward rate unbiasedness hypothesis. It estimates Fama regressions employing 1-month through to 10-year horizon data for the five most heavily traded US dollar currency pairs pre-crisis 1980–2006. In contrast with extant studies, it fully deals with the econometric problems of long horizon regressions by means of a novel heteroskedastic- and autocorrelation-consistent bootstrap. The regression results confirm a clear horizon effect in that the slope coefficient approaches unity as the forward contract maturity is extended. The puzzle disappears at the 3-year horizon and beyond for all currencies.     

Investing at home and abroad: Different costs,different people?

We investigate US households’ direct investment in stocks, bonds and liquid accounts and their foreign counterparts, in order to identify the different participation hurdles affecting asset investment domestically and overseas. To this end, we estimate a trivariate probit model with three further selection equations that allows correlations among unobservables of all possible asset choices. Our results point to the existence of a second hurdle that stock owners need to overcome in order to invest in foreign stocks. On the other hand, we find little evidence for additional pecuniary or informational costs associated with investment in foreign bonds and liquid accounts.     

Corporate payout policy in Australia and a test of the life‐cycle theory

We provide evidence on the frequency and size of payouts by Australian firms, and test whether the life‐cycle theory explains Australian corporate payout policies. Regular dividends remain the most popular mechanism for distributing cash to shareholders, despite a slight decline in the proportion of dividend payers since the relaxation of buyback regulations in 1998. Off‐market share buybacks return the largest amount of cash to shareholders. Dividend paying firms are larger, more profitable and have less growth options that nondividend paying firms. Consistent with the life‐cycle theory, we observe a highly significant relation between the decision to pay regular dividends and the proportion of shareholders’ equity that is earned rather than contributed.     

Collateral and capital structure

We develop a dynamic model of investment, capital structure, leasing, and risk management based on firms' need to collateralize promises to pay with tangible assets. Both financing and risk management involve promises to pay subject to collateral constraints. Leasing is strongly collateralized costly financing and permits greater leverage. More constrained firms hedge less and lease more, both cross-sectionally and dynamically. Mature firms suffering adverse cash flow shocks may cut risk management and sell and lease back assets. Persistence of productivity reduces the benefits to hedging low cash flows and can lead firms not to hedge at all.     

The structure and degree of dependence: A quantile regression approach

The copula function defines the degree of dependence and the structure of dependence. This paper proposes an alternative framework to decompose the dependence using quantile regression. We demonstrate that the methodology provides a detailed picture of dependence including asymmetric and non-linear relationships. In addition, changes in the degree or structure of dependence can be modeled and tested for each quantile of the distribution. The empirical part applies the framework to three different sets of financial time-series and demonstrates substantial differences in dependence patterns among asset classes and through time. The analysis of 54 global equity markets shows that detailed information about the structure of dependence is crucial to adequately assess the benefits of diversification in normal times and crisis times.     

Macroeconomic news, announcements, and stock market jump intensity dynamics

This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time-varying jump intensity, which is allowed to respond to such information. The day of the announcement, per se, is found to have little impact on jump intensities. Employment releases are an exception. However, when macroeconomic surprises are considered, inflation shocks show persistent effects while monetary policy and employment shocks reveal only short-lived effects. Also, the jump intensity responds asymmetrically to macroeconomic shocks. Evidence on macroeconomic variables relevance in explaining jump dynamics and improving volatility forecasts on event days is provided.     

Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model

This paper introduces the Smooth Transition version of FIGARCH model which is designed to account for both long memory and nonlinear dynamics in the conditional variance. Nonlinearity is introduced via a logistic transition function. The model can capture smooth changes in the volatility across different regimes as well as asymmetric response to negative and positive shocks and allows for nonzero thresholds. Simulations find that the Smooth Transition FIGARCH model outperforms the standard FIGARCH model when nonlinearity is present, and ignoring nonlinearity in the data may induce considerable costs in terms of bias and efficiency. Applications to exchange rate and stock market data show that the proposed model performs well both in-sample fit as well as in forecasting one-day ahead volatility.     

Stockholding: Participation, location, and spillovers

This paper provides the first joint analysis of household stockholding participation, location among stockholding modes, and participation spillovers. Our model matches observed participation, conditional and unconditional, and asset location patterns. We find that financial sophistication correlates strongly only with direct stockholding and mutual fund participation, while social interactions mainly influence stockholding through retirement accounts. Whether retirement account owners include stocks in their accounts strongly depends on owner characteristics, which is not the case with mutual fund owners and investment in stock funds. Stockholding is more common among retirement account owners, but mainly because of owner characteristics rather than of any participation spillovers from retirement account ownership.     

Are survey forecasts of individual and institutional investor sentiments rational?

We examine the effects of rational risk factors on investor sentiments. We find that institutional investor sentiments are more rational than individual investor sentiments. There are significant positive effects of, market return and dividend yield and negative effect of inflation on both types of sentiments. These risk factors have stronger effects on institutional than individual investor sentiments. Also, there are significant effects of term spread and HML on the institutional investor sentiments. The evidence suggests that linkages between sentiments and stock return stems from a combination of rational outlook and noise i.e. expectations that are not fully justified by information.     

The leverage effect puzzle: Disentangling sources of bias at high frequency

The leverage effect refers to the generally negative correlation between an asset return and its changes of volatility. A natural estimate consists in using the empirical correlation between the daily returns and the changes of daily volatility estimated from high frequency data. The puzzle lies in the fact that such an intuitively natural estimate yields nearly zero correlation for most assets tested, despite the many economic reasons for expecting the estimated correlation to be negative. To better understand the sources of the puzzle, we analyze the different asymptotic biases that are involved in high frequency estimation of the leverage effect, including biases due to discretization errors, to smoothing errors in estimating spot volatilities, to estimation error, and to market microstructure noise. This decomposition enables us to propose novel bias correction methods for estimating the leverage effect.     

A note on the importance of overnight information in risk management models

This paper examines the economic value of overnight information to users of risk management models. In addition to the information revealed by overseas markets that trade during the (domestic) overnight period, this paper exploits information generated via recent innovations in the structure of financial markets. In particular, certain securities (and associated derivative products) can now be traded at any time over a 24-h period. As such, it is now possible to make use of information generated by trading, in (almost) identical securities, during the overnight period. Of the securities that are available over such time periods, S&P 500 related products are by far the most actively traded and are, therefore, the subject of this paper. Using a variety of conditional volatility models that allow time-dependent information flow within (and across) three different S&P 500 markets, the results show that overnight information flow has a significant impact on the conditional volatility of daytime traded S&P 500 securities. Moreover (time-consistent) forecasts from models that incorporate overnight information are shown to have economic value to risk managers. In particular, Value-at-Risk (VaR) models based on these conditional volatility models are shown to be more accurate than VaR models that ignore overnight information.