Forward-rate target zones and exchange rate dynamics

Lin, Hwan C.

doi:10.1016/j.jimonfin.2008.02.009

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…where γ captures (the absolute value of) the semi-elasticity of money demand to the (nominal) domestic interest rate (in years) in a monetary exchange rate model for a small open economy (for details on the economic foundations of the model, see e.g. footnote 4 on page 29 in Svensson (1991a) or Section 2 in Lin (2008)). Within this setting, γ is derived from a log-functional form of the money demand equation.…”

Section: Monetary Model Of Exchange Ratesmentioning

confidence: 99%

Foreign Exchange Interventions Under a One-Sided Target Zone Regime and the Swiss Franc

Hertrich

2020

SSRN Journal

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From September 2011 to January 2015, the Swiss National Bank (SNB) implemented a minimum exchange rate regime (i.e. a one-sided target zone) vis-à-vis the euro to fight deflationary pressures in the aftermath of the Great Financial Crisis. During this period of unconventional monetary policy, the SNB faced mounting criticism from the media and the public on the sizable balance sheet risks that it was incurring. Motivated by this episode, I present a structural model embedded within the target zone framework developed by Krugman (1991) that allows monetary authorities to determine ex-ante the maximum size of foreign exchange market interventions that are expected to be necessary to implement and maintain a one-sided target zone. An empirical application of the proposed model to the aforementioned episode reveals that it is well suited to explain the actual size of these interventions and that, in January 2015, the SNB's euro purchases might indeed have been large without the abandonment of the minimum exchange rate regime, which is consistent with the official statements of the SNB in the aftermath of that episode.

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Section: Monetary Model Of Exchange Ratesmentioning

confidence: 99%

Foreign Exchange Interventions Under a One-Sided Target Zone Regime and the Swiss Franc

Hertrich

2020

SSRN Journal

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“…To carry out the simulations, the following risk factors were identified: sales quantities, commodity prices and the foreign exchange rate. The simulations are calculated using the geometric Brownian process, as in Postali and Picchetti (2006) and Lin (2008). In Equation (1), S i is the price or quantity of the variable i in a given time t , μ is the expected growth rate, and σ is the monthly volatility.…”

Section: The Risk Measurement and Decision Makingmentioning

confidence: 99%

Financial exposure and technology innovation investment

Quintella

Silva

Almeida³

et al. 2017

ARLA

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Purpose The purpose of this paper is to identify, measure and optimise financial risk and its effect on returns from innovation projects on an accrual basis and on a cash basis in a commodity industry. Design/methodology/approach A hypothetical case study, based on a real case, of a petrochemical commodity industry in Brazil was analysed with commodities pricing rules based on actual contracts. Earnings at risk (EaR) and cash flow at risk (CFaR) measures were applied, as well as a metric proposed in this paper called cash balance at risk (CBaR). Findings The paper demonstrates that financial risk measurement and optimisation are important issues in the decision-making process in the petrochemical industry. EaR, CFaR and CBaR measures are helpful when used alongside standard procedures of project evaluation. The findings also show that innovative technologies, in certain conditions, may act as “natural hedging”. It was found that the time delay between revenues and expenses leads to financial risk exposure to changes in prices and foreign exchange rates. Projects can use financing and hedging to boost their results. Originality/value An innovative project was compared with an expansion project in a petrochemical industry. A model for petrochemical commodities contract pricing was added in an analysis that included financing and hedging. The findings in this paper suggest that it is important to consider financial risk measures in project evaluation.

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“…One "shortcoming" of the model ( 55) is that, since we have (50), the drift µ(x) = −σ 2 p(x) is negative away from the boundaries, albeit it is small (compared with √ 2σ 2 π/L) as it is suppressed by γ ≪ 1. Therefore, in a long run, on average X t will slowly drift toward 0.…”

Section: Nonvanishing Potential and Driftmentioning

confidence: 99%

“…For a literature survey, see, e.g.,(Duarte et al, 2013). For a partial list (with some related literature, including on option pricing), see, e.g.,(Andersen et al, 2001),(Anthony and MacDonald, 1998),(Ayuso and Restoy, 1996),(Ball and Roma, 1994),(Bauer et al, 2009),(Beetsma and Van Der Ploeg, 1994),(Bekaert and Gray, 1998),(Bertola and Caballero, 1992),(Bertola and Svensson, 1993),(Black and Scholes, 1973),(Bo et al, 2011a(Bo et al, , 2001b,(Campa and Chang, 1996),(Carr et al, 1998),(Carr and Jarrow, 1990),(Carr and Linetsky, 2000),(Cavaliere, 1998),(Chinn, 1991),(Cornell, 2003), (Christensen et al, 1998), (De Jong, 1994), (De Jong et al, 2001), (Delgado and Dumas, 1992), (Dominquez and Kenen, 1992), (Driffill and Sola, 2006), (Duarte et al, 2010), (Dumas et al, 1995a, 1995b), (Edin and Vredin, 1993), (Edison et al, 1987),(Flood and Garber, 1991),,(Garman and Kohlhagen, 1983),(Grabbe, 1983),(Harrison, 1985),(Harrison and Pliska, 1981),(Honogan, 1998),(Hull and White, 1987),(Kempa and Nelles, 1999),(Klaster and Knot, 2002),(Klein and Lewis, 1993),(Koedijk et al, 1998),(Krugman, 1991(Krugman, , 1992,(Lai et al, 2008),(Larsen and Sørensen, 2007),(Lin, 2008), Söderlind, 1994a, 1994b),(Lindberg et al, 1993),…”

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confidence: 99%