相對於資本市場對於一國之經濟，外匯市場已日趨重要。在過去，已有許多研究，從總經面來觀察匯率的波動，筆者試著跳脫總經面而從投資者對風險的承受度角度去看外匯匯率的波動。因為在實務上，我們發現一個有趣的現象，就是當匯率波動度變大時，資產價格往往是下跌的。也就是當市場匯率波動度升高時，投資者傾向於保守，只尋求報酬較低但相對較安全之標的物。本研究從亞洲13個幣別分別對匯市、股市、利率、商品與信用等市場的風險波動度來做分析，以期能達到協助匯率預測的目標。本研究主要以14個風險變數來做分析，假設每個變數與匯率波動度之間存在的正負關係，最後再以迴歸係數是否顯著，來驗證我們的假設是否成立。 文中之資料除新興市場債券指數來自JP Morgan銀行外，其餘皆取自路透社及彭博社之歷史資料庫。 本研究發現，可以綜合出三點:(一)在亞洲貨幣中，投資人認為澳洲幣與紐西蘭幣相對於美元，是屬於高風險資產。(二)其它的亞洲貨幣，呈現相反的態勢，投資人普遍認為本國貨幣相對美元是安全性資產，當風險貼水增加時，投資人傾向持有本國貨幣。這種情形尤以台灣和日本為甚。(三) 本文一共蒐集五種波動度來衡量投資人心中風險貼水的變化，實證發現以美國S&P 500的波動度指數標VIX最具全面性的效果。 / To a nation’s economy, foreign exchange market has gained its importance over time in comparison to the capital market. There are already many studies that look at foreign exchange rate movement from a macroeconomic standpoint in the past. The author here is trying to leave macroeconomic behind and look at foreign exchange rate movement from investors’ risk aversion level point of view. The interesting phenomenon we found in the realistic setting is that when exchange rate volatility increases, the asset price usually decreases. In another word, when market exchange rate volatility increases, investors tend to be more conservative and seek investment targets with lower risks and lower returns. This study analyzes 13 Asian currencies in relation to the volatility of foreign exchange market, stock market, interest rate, commodity market and credit market in hope to be able to forecast foreign exchange rates. This study uses 14 risk variables for its analysis. We assume each risk variable has a positive or negative relationship with foreign exchange rate volatility then we run multiple regression analysis to check the relevance of each variable and to validate our assumptions. All data came from Reuters and Bloomberg historic database, with the exception of Developing Market Bond Index which was obtained from JP Morgan Bank. The result of this study can be summarized as 3 findings: 1. Investors believe that when comparing with USD dollar, AUD and NZD are the risk assets. 2. Except AUD and NZD, investors think rest of the Asia currencies are risk assets. When risk premium increasing; the investors would like to have local currencies instead of USD. 3. The study uses 5 different volatilities from different markets to test the risk appetite from investors. It turns out the US stock market VIX index has most obviously link with Asia Currency.

<p>In Chapter 1, I review the existing literature on the investor and contracting usefulness of other comprehensive income (OCI) components. In Chapter 2, I perform empirical tests focused on one aspect of investor usefulness of accounting information: risk-relevance. I examine whether OCI component volatilities are associated with investors' returns volatility using a sample of bank holding companies from 1998 to 2012. The results indicate that the volatilities of unrealized gains and losses on available-for-sale (AFS) securities and cash-flow hedges, typically deemed beyond managers' control, are negatively associated with risk, while volatilities of OTTI losses, over which managers have relatively more control, are positively associated with risk. The results are consistent with investors perceiving the volatility of non-OTTI AFS unrealized gains and losses as relatively less important, less risky, or less risk-relevant, than the volatility of OTTI losses, and perceiving the volatility of OTTI losses as an informative signal about risk. In Chapter 3, I find that Tier 1 Capital including more components of accumulated other comprehensive income (AOCI), as stipulated by Basel III, is no more volatile than pre-Basel-III Tier 1 Capital, and that the volatilities of the AOCI components new to Tier 1 Capital are not positively associated with risk. In Chapter 4, I discuss future research.</p> / Dissertation

This article identifies the best models for forecasting the volatility of daily exchange returns of developing countries. An emerging consensus in the recent literature focusing on industrialized countries has noted the superior performance of the Fractionally Integrated Generalized Autoregressive Conditionally Heteroscedastic (FIGARCH) model in the case of industrialized countries, a result that is reaffirmed here. However, we show that when dealing with developing countries' data the IGARCH model results in substantial gains in terms of the in-sample results and out-of-sample forecasting performance. (authors' abstract)

The day-of-the-week effect is a market anomaly that manifests as the cyclical behaviour of traders in the market. This market anomaly was first observed by M.F.M. Osborne (1959). The literature distinguishes between two types of cyclical effects in the market: the cyclical pattern of mean returns and the cyclical pattern of volatility in returns. This dissertation studies and reports on cyclical patterns in the South African market, seeking evidence of the existence of the day-of-the-week effect. In addition, the dissertation aims to investigate the implications of such an effect on hedge fund managers in South Africa. The phenomenon of cyclical volatility and mean returns patterns (day-of-the-week effect) in the South African All-share index returns are investigated by making use of four generalised heteroskedastic conditional autoregressive (GARCH) models. These were based on Nelson's (1991) Exponential GARCH (EGARCH) models. In order to account for the risk taken by investors in the market Engle et al's, (1987) 'in-Mean' (risk factor) effects were also incorporated into the model. To avoid the dummy variable trap, two different approaches were tested for viability in testing for the day-of- the-week effect. In the first approach, one day is omitted from the equation so as to avoid multi-colinearity in the model. The second approach allows for the restriction of the daily dummy variables where all the parameters of the daily dummy variables adds up to zero. This dissertation found evidence of a mean returns effect and a volatility effect (day-of-the- week effect) in South Africa's All-share index returns data (where Wednesdays have been omitted from the GARCH equations). This holds significant implications for hedge fund managers. as hedge funds are very sensitive to volatility patterns in the market, because of their leveraged trading activities. As a result of adverse price movements, hedge fund managers employ strict risk management processes and constantly rebalance their portfolios according to a mandate, to avoid incurring losses. This rebalancing typically involves the simultaneous opening of new positions and closing out of existing positions. Hedge fund managers run the risk of incurring losses should they rebalance their portfolios on days on which the volatility in market returns is high. This study proves the existence of the day-of-the-week effect in the South African market. These results are further confirmed by the evidence of the trading volumes of the JSE's All-share index data for the period of the study. The mean returns effect (high mean returns) and low volatility found on Thursdays, coincide with the evidence that trading volumes on the JSE on Thursdays are the highest of all the days of the week. The volatility effect on Fridays, (high volatility in returns) is similarly correlated with the evidence of the trading volumes found in the JSE's All-share index data for the period of the study. Accordingly. hedge fund managers would be advised to avoid rebalancing their portfolios on Fridays, which show evidence of high volatility patterns. Hedge fund managers are advised to rather rebalance their portfolios on Thursdays, which show evidence of high mean returns patterns, low volatility patterns and high liquidity. / Thesis (M.Com. (Risk Management))--North-West University, Potchefstroom Campus, 2006.

This thesis explores numerical methods for solving nonlinear partial differential equations (PDEs) that arise in option pricing problems. The goal is to develop or identify robust and efficient techniques that converge to the financially relevant solution for both one and two factor problems. To illustrate the underlying concepts, two nonlinear models are examined in detail: uncertain volatility and passport options. For any nonlinear model, implicit timestepping techniques lead to a set of discrete nonlinear equations which must be solved at each timestep. Several iterative methods for solving these equations are tested. In the cases of uncertain volatility and passport options, it is shown that the frozen coefficient method outperforms two different Newton-type methods. Further, it is proven that the frozen coefficient method is guaranteed to converge for a wide class of one factor problems. A major issue when solving nonlinear PDEs is the possibility of multiple solutions. In a financial context, convergence to the viscosity solution is desired. Conditions under which the one factor uncertain volatility equations are guaranteed to converge to the viscosity solution are derived. Unfortunately, the techniques used do not apply to passport options, primarily because a positive coefficient discretization is shown to not always be achievable. For both uncertain volatility and passport options, much work has already been done for one factor problems. In this thesis, extensions are made for two factor problems. The importance of treating derivative estimates consistently between the discretization and an optimization procedure is discussed. For option pricing problems in general, non-smooth data can cause convergence difficulties for classical timestepping techniques. In particular, quadratic convergence may not be achieved. Techniques for restoring quadratic convergence for linear problems are examined. Via numerical examples, these techniques are also shown to improve the stability of the nonlinear uncertain volatility and passport option problems. Finally, two applications are briefly explored. The first application involves static hedging to reduce the bid-ask spread implied by uncertain volatility pricing. While static hedging has been carried out previously for one factor models, examples for two factor models are provided. The second application uses passport option theory to examine trader compensation strategies. By changing the payoff, it is shown how the expected distribution of trading account balances can be modified to reflect trader or bank preferences.

The Vanna-Volga (VV) method has been in wide use as one of the major tools for several years among foreign exchange (FX) trading desks. Despite its popularity, the properties of the VV method are not well studied and understood. This thesis attempts to understand better why and when the VV method makes sense, and how to use it better. Often under practical circumstances the state of calibration can be described as being frequent but imperfect. To take advantage of this level of calibration, we studied the properties and benefits of the Karasinski method, and extended this method to a few useful applications. We have found that the Karasinski method, if used with a reasonably calibrated model, can provide significant performance improvement over the VV method.The VV and Karasinski chapters contain most of the original research in this thesis; there are a wealth of discoveries made in these chapters. Novel methods and applications related to the VV and Karasinski methods are proposed, and some of which can be readily applied to the practical trading environment. To make the VV and Karasinski methods work well in practice, the numerical issues for computing the price and Greeks have been carefully addressed with finite difference schemes that are second-order convergent and fast to compute. As an example of easy-to-compute but difficult-to-calibrate model candidates for the Karasinski method, the Multi-Heston model has been discussed too. A sound computational preparation enables the VV and in particular Karasinski methods to enjoy high viability as being fast, efficient and practical. This thesis is tailored to the purpose of making a detailed study on these useful methods whose great potential has not been adequately understood and fully realised.

The growth of the financial risk management industry has been motivated by the increased volatility of financial markets combined with the rapid innovation of derivatives. Since the 1970s, several financial crises have occurred globally with devastating consequences for financial and non-financial institutions and for the real economy. The most recent US subprime crisis led to enormous losses for financial and non-financial institutions and to a recession in many countries including the US and UK. A common lesson from these crises is that advanced financial risk management systems are required. Financial risk management is a continuous process of identifying, modeling, forecasting and monitoring risk exposures arising from financial investments. The Value at Risk (VaR) methodology has served as one of the most important tools used in this process. This quantitative tool, which was first invented by JPMorgan in its Risk-Metrics system in 1995, has undergone a considerable revolution and development during the last 15 years. It has now become one of the most prominent tools employed by financial institutions, regulators, asset managers and nonfinancial corporations for risk measurement. My PhD research undertakes a comprehensive and practical study of market risk modeling in modern finance using the VaR methodology. Two newly developed risk models are proposed in this research, which are derived by integrating volatility modeling and the quantile regression technique. Compared to the existing risk models, these two new models place more emphasis on dynamic risk adjustment. The empirical results on both real and simulated data shows that under certain circumstances, the risk prediction generated from these models is more accurate and efficient in capturing time varying risk evolution than traditional risk measures. Academically, the aim of this research is to make some improvements and extensions of the existing market risk modeling techniques. In practice, the purpose of this research is to support risk managers developing a dynamic market risk measurement system, which will function well for different market states and asset categories. The system can be used by financial institutions and non-financial institutions for either passive risk measurement or active risk control.

In this note we develop a Taylor rule based empirical exchange rate model for eleven major currencies that endogenously determines the number of structural breaks in the coefficients. Using a constant parameter specification and a standard time-varying parametermodel as competitors reveals that our flexible modeling framework yields more precise density forecasts for all major currencies under scrutiny over the last 24 years. / Series: Department of Economics Working Paper Series

We propose a large-scale Bayesian VAR model with factor stochastic volatility to investigate the macroeconomic consequences of international uncertainty shocks on the G7 countries. The factor structure enables us to identify an international uncertainty shock by assuming that it is the factor most correlated with forecast errors related to equity markets and permits fast sampling of the model. Our findings suggest that the estimated uncertainty factor is strongly related to global equity price volatility, closely tracking other prominent measures commonly adopted to assess global uncertainty. The dynamic responses of a set of macroeconomic and financial variables show that an international uncertainty shock exerts a powerful effect on all economies and variables under consideration. / Series: Department of Economics Working Paper Series

This paper provides an empirical assessment of whether the world economy has become smaller in terms of economic distance over the last decades. We adopt a cross-sectional spatial econometric approach, relating domestic output volatility to (distance-weighted averages of) other countries' output volatility, using a sample of 135 countries and rolling 10-year time windows over the period 1955 to 2006. Using descriptive measures, test statistics, and spatial econometric estimates, we find that cross-country interdependence was virtually insignificant in the early post-war period but has increased strongly from the mid-1960s to the mid-1980s and remained at a high level since then. Results for the most recent period suggest that common shocks to output volatility have a magnified impact and roughly quadruplicate through international spillover effects, which are transmitted through both trade and financial openness.