A Perspective on Modelling the Australian Real Trade Weighted Index since the Float

Since the deregulation of the Australian dollar market in December 1983, considerable effort has been devoted by the central bank to understanding movements in the value of the currency. As the Reserve Bank of Australia (RBA) has a pivotal role to play in currency markets, attention has been focussed on the modelling techniques used by the Bank's researchers in this process. This paper examines the ancestral development of the current model of the Australian Trade Weighted Index ( rtwi ) used at the RBA, as specified in Beechey et al. (2000). Estimates and forecasting evaluations of the various models imply that only the relationships between the rtwi , the terms of trade and interest differentials hold consistently, providing the empirical foundation for the current RBA model.     

Monetary Policy in Illiquid Markets: Options for a Small Open Economy

Two impediments to effective monetary policy operation include illiquidity in bond markets and the zero bound of interest rates. Under these conditions alternative means of enacting monetary policy may be required. This paper empirically explores policy options implemented through equity and currency markets that will generate similar inflation responses at different time horizons. In terms of GDP loss the least costly means of achieving a particular long run inflation outcome is via the current monetary policy arrangements. Currency market alternatives are volatile but less expensive than the equity market in terms of output loss for short term inflation horizons.

After‐hours trading in equity futures markets

Although it is well known that electronic futures data absorb news (slightly) in advance of spot markets the role of the electronic futures movement in out‐of‐hours trading has not previously been explored. The behavior of the 24‐hour trade in the S&P 500 and NASDAQ 100 futures market reveals the important role of these markets in absorbing news releases occurring outside of normal trading hours. Peaks in volume and volatility in this market occur in conjunction with U.S. 8:30 A.M. EST news releases, before the opening of the open‐outcry markets, and in a less pronounced fashion immediately post‐close the open‐outcry market. Price impact in these markets is statistically higher in the post‐close than in the pre‐open periods. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 29:114–136, 2009     

Empirical modelling of contagion: a review of methodologies

Chris Attfield, Michaela Glod and Jessica James suggest an approach to optimize hedging decisions for foreign exchange investments.     

Contagion in international bond markets during the Russian and the LTCM crises

The Russian bond default in August 1998 and the long-term capital management (LTCM) recapitalization announcement in the following month represent an unusual period of volatility in international bond markets with bond spreads increasing dramatically across the globe. Using a latent factor model and a new data set spanning bond markets across Asia, Europe and the Americas, we quantify the contribution of contagion to the spread of these two crises. The maximum amount of contagion experienced by any of the countries investigated is about 17% of total volatility in bond spreads, with the main effects due to the Russian crisis. The results also show that both emerging and developed markets experienced contagion during the period.     

Identifying contagion

Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this we propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common factor(s). Within a portfolio mimicking factor framework this can be identified using moment conditions. We use this framework to identify contagion in three illustrations involving both single and multiple factor specifications: to the Asian currency markets in 1997–1998, to US sectoral equity indices in 2007–2009 and to the CDS (credit default swap) market during the European sovereign debt crisis of 2010–2013. The results reveal the extent to which contagion effects may be masked by not accounting for the sources of changed volatility apparent in simple measures such as correlation.     

Testing for mutually exciting jumps and financial flights in high frequency data

We propose a new nonparametric test to identify mutually exciting jumps in high frequency data. We derive the asymptotic properties of the test statistics and show that the tests have good size and reasonable power in finite sample cases. Using our mutual excitation tests, we empirically characterize the dynamics of financial flights in forms of flight-to-safety and flight-to-quality. The results indicate that mutually exciting jumps and risk-off trades mostly occur in periods of high market stress. Flight-to-safety episodes (from stocks to gold) arrive more frequently than do flight-to-quality spells (from stocks to bonds). We further find evidence that reverse cross-excitations or seeking-return-strategies exhibit significant asymmetry over the business cycle, reflecting the fact that investors appear to be selling gold – rather than bonds – to invest in stocks during good market conditions.     

Equity portfolio diversification with high frequency data

Investors wishing to achieve a particular level of diversification may be misled on how many stocks to hold in a portfolio by assessing the portfolio risk at different data frequencies. High frequency intradaily data provide better estimates of volatility, which translate to more accurate assessment of portfolio risk. Using 5-min, daily and weekly data on S&P500 constituents for the period from 2003 to 2011, we find that for an average investor wishing to diversify away 85% (90%) of the risk, equally weighted portfolios of 7 (10) stocks will suffice, irrespective of the data frequency used or the time period considered. However, to assure investors of a desired level of diversification 90% of the time (in contrast to on average), using low frequency data results in an exaggerated number of stocks in a portfolio when compared with the recommendation based on 5-min data. This difference is magnified during periods when financial markets are in distress, as much as doubling during the 2007–2009 financial crisis.     

Financial integration and the construction of historical financial data for the Euro Area

Time series analysis for the Euro Area requires the availability of sufficiently long historical data series, but the appropriate construction methodology has received little attention. The benchmark dataset, developed by the European Central Bank for use in its Area Wide Model (AWM), is based on fixed-weight aggregation across countries with historically distinct monetary policies and financial markets of varying international importance. This paper proposes a new methodology for producing back-dated financial series for the Euro Area, that is based on the time-varying distance of periphery countries from core countries with respect to monetary integration. Historical decompositions of the residuals of vector autoregressive models of the Euro Area economy are then used to explore and compare the monetary policy implications of using the new methodology versus the use of AWM fixed weight series.