The 12 reference contexts in paper Angelo Ranaldo, Paul Söderlind (2007) “Safe Haven Currencies” / RePEc:usg:dp2007:2007-22

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    By changing the time granularity of our analysis, we provide evidence that this risk-return transmission mechanism is operational from an intraday basis up to several days. Our study is related to several fields of the financial literature. First, the literature on safe-haven currencies provides only limited and occasional evidence of this phenomenon. For instance,
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    Kaul and Sapp (2006)
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    show that the US dollar was used as a safe vehicle around the millennium change. Here, we provide empirical evidence that safe-haven effects override specific events and market conditions. Thus, sporadic loss and gain of safe-haven attributes of a given currency is only the visible part of an iceberg.
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    Third, our study provides empirical support to flight-to-quality and contagion phenomena. The flight-to-quality literature argues that an increase in perceived riskiness engenders conservatism and demand for safety (e.g.
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    Caballero and Krishnamurthy (2007)).
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    At the same time, the contagion literature shows that risk and market crashes spill over across countries, international markets and, possibly, asset classes (e.g. Hartmann, Straetmans, and De Vries (2001)).
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    Therefore, our study deliver some insights about the recent literature on liquidity and price changes’ commonality across asset classes (e.g. Chordia, Sarkar, and Subrahmanyam (2005)), adverse liquidity spirals between liquidity drains, wealth reduction and funding constraints
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    (Brunnermeier and Pedersen (2007)), and
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    market liquidity declines as volatility increases in the spirit of the “flight to liquidity” phenomenon. Finally, our study adds to the empirical market microstructure field. The previous literature in this area has showed that order flow significantly determines exchange rates (e.g.
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    between liquidity drains, wealth reduction and funding constraints (Brunnermeier and Pedersen (2007)), and market liquidity declines as volatility increases in the spirit of the “flight to liquidity” phenomenon. Finally, our study adds to the empirical market microstructure field. The previous literature in this area has showed that order flow significantly determines exchange rates (e.g.
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    Evans and Lyons (2002b)) and
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    that there are important linkages across currency pairs (e.g. Evans and Lyons (2002a)). On the basis of a large and long high-frequency database, our 4 work add to this literature by showing that price formation processes across forex, equity and bond markets are inter-connected even on an intraday basis.
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    The previous literature in this area has showed that order flow significantly determines exchange rates (e.g. Evans and Lyons (2002b)) and that there are important linkages across currency pairs (e.g.
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    Evans and Lyons (2002a)).
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    On the basis of a large and long high-frequency database, our 4 work add to this literature by showing that price formation processes across forex, equity and bond markets are inter-connected even on an intraday basis.
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    This sheds new light on parallel market forces and synchronised price discovery characterising different markets and investment categories. Furthermore, our study shows that realised volatility measures in the spirit of e.g.
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    Bollerslev and Andersen (1998)
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    are able to proxy for the perceived market risk and that transient market volatility has a significant role in determining the price formation process of safe-haven currencies. Two main results emerge from our work.
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    We use the most actively traded nearest-to-maturity or cheapest-to-delivery futures contract, switching to the nextmaturity contract five days before expiration. If no trades occur in a given 5-minute interval, we copy down the last trading price in the previous time interval (see Andersen, Bollerslev, Diebold, and Vega (2004) and
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    Christiansen and Ranaldo (2007)).
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    These futures markets have overnight non-trading times. For theintradayanalysis we try to fill the gaps as far as possible. Unfortunately, this proved difficult for the bond market data. However, for the equity market we were able to construct a nearly round-theclock equity market time series by combining equity futures data from different regions.
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    three-hour horizon, the Treasury note futures returns are only available for 4 of the 8 three-hour intervals of a day (and night), while the most of the other data is available for 7 or 8 intervals. To avoid loosing to much data in the intraday regressions, we do two things. First, the lagged Treasury note futures is excluded (that is,ˇ5in (2) is restricted to zero). Second, we apply the
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    Griliches (1986)
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    two-step approach to handle the still missing data points of the Treasury note futures. Effectively, this means that we estimate theˇ2coefficient in (2) on the 4 three-hour intervals with complete data, but the other coefficients on the 7 or 8 three-hour intervals.
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    The EUR and JPY are mixed cases, since the JPY/EUR rate appreciates when the S&P strengthens and the Treasury note futures weakens (opposite to the CHF/EUR pattern), but it also appreciates when the currency market volatility increases (similar to the CHF/EUR pattern). These results seem to corroborate the traditional view of the Swiss franc as a safehaven asset.
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    Kugler and Weder (2004)
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    find that Swiss franc denominated assets ave lower returns than comparable assets denominated in other currencies. In the spirit of our study, this may be due to the safe-haven risk premium inherent in Swiss franc denominated assets.
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    Table 3reports results from estimating the regression equation (2) (with CHF/USD as the dependent variable) for different horizons: from 3 hours up to 4 days. For the intraday data we use a global equity series (NIKKEI, DAX, and S&P) instead of only S&P to get an almost round-the-clock series (see Section 3) and apply the
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    Griliches (1986)
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    twostep approach to handle the still missing data points of the Treasury note futures (see Section 4). The safe have effect is clearly visible on all these horizons, even if magnitude of the coefficients of S&P and currency market volatility is considerably smaller at the shorter horizons—and seem to peak around 1 to 2 days.
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    The t-statistics are based on a Newey-West estimator with two lags. See Table 1 for details on the data. The regressions on hourly data do not include the lagged Treasure notes futures as a regressor, and apply
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    Griliches (1986)
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    two-step approach to handle the still missing data points for the Treasury notes. into the Swiss franc value at any time granularity. This suggests the genuine character for the Swiss franc as a safe asset.
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    &P Effect of Treasure notes 0.5 0.2 0 −0.5 0 −1 −0.2 −505 −2−101 S&P returns, % Treasury notes returns, % Effect of FX volatility 0.2 0 −0.2 −0.4 −0.6 −0.8 −0.500.5 PX volatility Figure 5:Semiparametric estimates of effect on CHF/USD depreciation.This figure shows results estimating a sequence partial linear models,ytDx01tˇCg.x2t/Cut, with the CHD/USD depreciations as the dependent variable (see
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    Pagan and Ullah (1999)).
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    The first subfigure shows the non-linear part,g.x2t/, wherex2tis the S&P returns and all other regressors are assumed to have linear effects. The second subfigure instead allows the Treasury futures returns to have nonlinear effects, while the third subfigure allows the FX volatility to have have nonlinear effects.
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