International Finance and Risk Management

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Since the last time you logged in our privacy statement has been updated. We want to ensure that you are kept up to date with any changes and as such would ask that you take a moment to review the changes. You will not continue to receive KPMG subscriptions until you accept the changes. Close Hi! A while ago, researchers at the Wharton School surveyed companies on their risk management practices, including derivatives uses.

Finance and Risk Management MSc

Of the firms surveyed, responded. Not surprisingly, the survey found that companies use a range of methods and have a variety of reasons for using derivatives. It was also clear that not all risks that were managed were necessarily completely removed. About half of the respondents reported that they use derivatives as a risk-management tool.

One-third of derivative users actively take positions reflecting their market views, thus they may be using derivatives to increase risk rather than reduce it.

Financial Risk and Its Types

Of course, not only derivatives are used to manage risky cash flows. Companies can also rely on good old-fashioned techniques such as the physical storage of goods i. Not everyone chooses to manage risk, and risk management approaches differ from one firm to the next. This partly reflects the fact that the risk management goals differ across firms.

In particular, some firms use cash-flow volatility, while others use the variation in the value of the firm as the risk management object of interest. It is also generally found that large firms tend to manage risk more actively than do small firms, which is perhaps surprising as small firms are generally viewed to be more risky. However, smaller firms may have limited access to derivatives markets and furthermore lack staff with risk management skills.

The overall answer to this question appears to be yes. Analysis of the risk management practices in the gold mining industry found that share prices were less sensitive to gold price movements after risk management. Similarly, in the natural gas industry, better risk management has been found to result in less variable stock prices.

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International Finance and Risk Management

A study also found that risk management in a wide group of firms led to a reduced exposure to interest rate and exchange rate movements. Although it is not surprising that risk management leads to lower variability—indeed the opposite finding would be shocking—a more important question is whether risk management improves corporate performance. Again, the answer appears to be yes. Researchers have found that less volatile cash flows result in lower costs of capital and more investment. It has also been found that a portfolio of firms using risk management would outperform a portfolio of firms that did not, when other aspects of the portfolio were controlled for.

Similarly, a study found that firms using foreign exchange derivatives had higher market value than those who did not. The evidence so far paints a fairly rosy picture of the benefits of current risk management practices in the corporate sector. However, evidence on the risk management systems in some of the largest US commercial banks is less cheerful.

Several recent studies have found that while the risk forecasts on average tended to be overly conservative, perhaps a virtue at certain times, the realized losses far exceeded the risk forecasts. Importantly, the excessive losses tended to occur on consecutive days. Thus, looking back at the data on the a priori risk forecasts and the ex ante loss realizations, we would have been able to forecast an excessive loss tomorrow based on the observation of an excessive loss today.

This serial dependence unveils a potential flaw in current financial sector risk management practices, and it motivates the development and implementation of new tools such as those presented in this book. This question may also be stated as: How much risk is the facility willing to accept versus the cost of reducing that risk? The best answer to this question should consider all risks to the enterprise from all endeavors.

Thus, security is only one component of risk to the enterprise and must allocate resources within the larger risk picture. The facility or corporate Chief Risk Officer must still combine all of the various risks and help the corporation manage total risk. While the security department may be able to aid in mitigation of risk in other areas, the security organization is only one of many functions that must be depended on to assure that the corporate enterprise manages and limits their risk exposure.

Given limited resources to be applied to address all risks, each application of a portion of those resources must be carefully and analytically evaluated to ensure a balanced risk. Torben G. Francis X. Diebold, in Handbook of the Economics of Finance , We have attempted to demonstrate the power and potential of dynamic financial econometric methods for practical financial risk measurement and management.

We have surveyed the large literature on high-frequency volatility modeling, interpreting and unifying the most important and intriguing results of practical relevance. Key points include:. Reliable risk measurement requires a conditional density model that allows for time-varying volatility. Successful risk measurement may be achieved through the use of univariate density models directly for portfolio returns. GARCH volatility models offer a convenient and parsimonious framework for modeling key dynamic features of such portfolio returns, including volatility mean reversion, long memory, and asymmetries.

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Successful risk management, in contrast, requires a fully specified multivariate density model. In that regard, standard multivariate models are too heavily parameterized to be useful in realistic medium- and large-scale financial market contexts. In medium-scale financial contexts, recently developed multivariate GARCH models are likely to be useful. In very large-scale financial contexts, more structure must be imposed, such as decoupling variance and correlation dynamics.

In all cases, resampling methods applied to standardized returns are an attractive strategy for accommodating conditionally non-normal returns.


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Volatility measures based on high-frequency return data hold great promise for practical risk management, as realized volatility and correlation measures produce more accurate risk assessments and forecasts than their conventional competitors. Because high-frequency information is only available for highly liquid assets, a base-asset factor approach may sometimes be useful. The business cycle emerges as a key macroeconomic fundamental driving risk in a variety of markets, including equities and bond yields. Among other things, this means that our emphasis on conditioning applies not only at the short horizons typically daily stressed in Sections 2 and 3 , but also at much longer horizons, once the information set is appropriately broadened to include macro fundamentals as opposed to just past returns.

Details on the asymmetric t distribution considered here can be found in Hansen , Fernandez and Steel , and Jondeau and Rockinger Hansen and Jondeau and Rockinger also discuss time-varying skewness and kurtosis models. Applications of extreme value theory to financial risk management is discussed in McNeil Huisman et al. Brooks et al. Multivariate extensions to the univariate EVT analysis considered here can be found in Longin , Longin and Solnik , and Poon et al. The expected shortfall measure for the Cornish-Fisher approximation is developed in Giamouridis In the spirit of the Cornish-Fisher approach, Jondeau and Rockinger develop a Gram-Charlier approach to return distribution modeling.

Many alternative conditional distribution approaches exist. Kuerster et al. Artzner et al. Studying dynamic portfolio management based on ES and VaR , Basak and Shapiro found that when a large loss does occur, ES risk management leads to lower losses than VaR risk management.

Cuoco et al. Both Basak and Shapiro and Cuoco et al. Chen and Taylor consider nonparametric ES methods. As the preceding literature review indicates, much has been written about the definition, sources, consequences, and assessment methods for SCRM. Therefore, I focus on risk management techniques in discussing avenues for future research, with an emphasis on financial aspects of risk management for the supply chain, as there are already a large number of SCRM studies from an operations management perspective.

FOREIGN EXCHANGE RISK MGMT TECHNIQUES INTERNATIONAL FINANCE

I take for granted the albeit not universally accepted definitions, sources, consequences, and assessment frameworks of supply chain risks. I do not concern myself with strategic, operational, or organizational decisions that deal with supply chain risks, though these and financial risk management are interrelated. Recent studies such as Cunat , Costello , and Gamba and Triantis suggest that there is potential to expand the scope of SCRM to include not only operational decisions, but also investment, financing, and governance issues including corporate disclosure and contractual relations in the supply chain.

The existence of a large theoretical literature and well-established methodologies in these fields can provide both the theories and empirical methods to guide future research in SCRM. As de Zegher, Iancu, and Lee point out, in complex supply chains the benefits and costs of technological innovations do not always accrue equitably to all parties and, thus, their adoption may critically depend on sourcing relationships and incentives. There is a need to study ways to create mutual benefit in decentralized value chains, where suppliers bear the costs of new technologies while benefits accrue primarily to buyers.

While de Zegher et al. In a paper related to the aforementioned issues, Almeida, Hankins, and Williams analyze purchase obligations POs , or contractual obligations between a downstream firm and an upstream firm, as a financial risk management tool. They show that POs with suppliers are a corporate hedging tool, just like traded derivatives, that downstream companies commonly use to manage price fluctuations and hold-up problems between suppliers and customers. Given the ready availability of data about POs or other types of risk-related data and customer—supply relationships among publicly listed firms Cen et al.

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