風險管理和金融衍生品外文文獻翻譯（節(jié)選）

1、<p>　　1200單詞，7200英文字符,2000漢字</p><p>　　文獻出處：Hammoudeh S, McAleer M. Risk management and financial derivatives: An overview[J]. The North American Journal of Economics and Finance, 2013, 25: 109-115.</

2、p><p>　　http://www.wenku1.com/news/DA84F6D0ECDAFC3D.html</p><p><b>　　原文</b></p><p>　　Risk management and financial derivatives:An overview</p><p>　　Shawkat ; M

3、ichael</p><p><b>　　Abstract</b></p><p>　　Risk management is crucial for optimal portfolio management. One of the fastest growing areas in empirical finance is the expansion of financ

4、ial derivatives. The purpose of this special issue on “Risk Management and Financial Derivatives” is to highlight some areas in which novel econometric, financial econometric and empirical finance methods have contribute

5、d significantly to the analysis of risk management, with an emphasis on financial derivatives, specifically conditional correlations and vo</p><p>　　Keywords: Risk management; Optimal portfolios; Financial d

6、erivatives; Financial econometrics; Options; Futures;Volatility; Spillovers; Hedging; Default; Risk premia; Claims replication</p><p>　　1. Introduction</p><p>　　外文翻譯文獻 Risk management is crucia

7、l for optimal portfolio management. One of the fastest growing areas in empirical finance is the expansion of financial derivatives. While some of the key issues underlying risk and portfolio management are reasonably we

8、ll understood, many of the technical and empirical issues underlying the creation and movements in financial derivatives are less well understood.</p><p>　　It is our hope that the interesting, invaluable and

9、 innovative papers in this special issue will encourage others to undertake research in a variety of challenging areas associated with the exciting and rapidly expanding areas of risk management and financial derivatives

10、.</p><p>　　2. Overview</p><p>　　In the first paper, “Conditional Correlations and Volatility Spillovers Between Crude Oil and Stock Index Returns”, Chia-Lin Chang (National Chung Hsing Universit

11、y, Taiwan), Michael McAleer (Erasmus University Rotterdam, The Netherlands) and Roengchai Tansuchat (Maejo University, Thailand) investigate the conditional correlations and volatility spillovers between the crude oil an

12、d financial markets, based on crude oil returns and stock index returns.</p><p>　　Daily returns from January 2, 1998 to November 4, 2009 of the crude oil spot, forward and futures prices from the WTI and Bre

13、nt markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analyzed using Bollerslev's CCC model, Ling and McAleer's VARMA-GARCH model, McAleer, Hoti and Chan's VARMA-AGARCH model, a

14、nd Engle's DCC model.</p><p>　　Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the condi

15、tional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant c

16、onditional correlations is not supported empirically.</p><p>　　Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the cr

17、ude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that</p><p>　　VARMA-AGARCH is superior to VARMA-GARC

18、H and CCC. The estimation and analysis of the volatility and conditional correlations between crude oil returns and stock index returns can provide useful information for investors, oil traders and government agencies th

19、at are concerned with the crude oil and stock markets, especially regarding optimal hedging across the two markets.</p><p>　　As the second paper, Coenraad Labuschagne (University of the Witwatersrand, South

20、Africa) and Theresa Offwood (University of the Witwatersrand, South Africa) analyze the theoretical and practical issue of “Pricing Exotic Options Using the Wang Transform”. The Wang transform allows for a simple, yet in

21、tuitive approach to pricing options with underlying based on geometric Brownian motion. The authors show how the approach by Hamada and Sherris (2003) can be used to price some exotic options. Examp</p><p>　

22、　Chia-Lin Chang (National Chung Hsing University, Taiwan), Juan-Angel Jimenez-Martin (Complutense University of Madrid, Spain), Michael McAleer (Erasmus University Rotterdam, The Netherlands) and Teodosio Perez-Amaral (C

23、omplutense University of Madrid, Spain) analyze “The Rise and Fall of S&P500 Variance Futures” in the third paper. Modeling, monitoring and forecasting volatility are indispensible to sensible portfolio risk manageme

24、nt.</p><p>　　The volatility of an asset of composite index can be traded by using volatility derivatives, such as volatility and variance swaps, options and futures. The most popular volatility index is VIX,

25、 which is a key measure of market expectations of volatility, and hence also an important barometer of investor sentiment and market volatility. Investors interpret the VIX cash index as a “fear” index, and of VIX option

26、s and VIX futures as derivatives of the “fear” index.</p><p>　　In the fourth paper, entitled “Predicting Volatility Using Markov Switching Multifractal Model: Evidence from S&P 100 Index and Equity Optio

27、ns”, Wen-I Chuang (National Taiwan University, Taiwan), Teng-Ching Huang (National Taiwan University of Sciences and Technology, Taiwan) and Bing-Huei Lin (National Chung Hsing University, Taiwan) evaluate the performanc

28、e of the ability of Markov-switching multifractal (MSM), implied, GARCH, and historical volatilities to predict realized volatility for both</p><p>　　First, the authors find that the ability of MSM and GARCH

29、 volatilities to predict realized volatility is better than that of implied and historical volatilities for both the index and equity options. Second, equity option volatility is more difficult to be forecast than index

30、option volatility. Third, both index and equity option volatilities can be better forecast during non-global financial crisis periods than during global financial crisis periods. Fourth, equity option volatility exhibits

31、 di</p><p>　　3. Final remarks</p><p>　　It is our hope that the collection of interesting, invaluable and innovative papers in this special issue by some of the leading experts in the field of “R

32、isk Management and Financial Derivatives” will be of wide interest to theoreticians and practitioners alike in risk and portfolio management, empirical finance and financial econometrics, and will encourage others to und

33、ertake research in a variety of challenging areas associated with the exciting and rapidly expanding areas of risk management</p><p><b>　　譯文</b></p><p>　　風險管理和金融衍生品:一個綜述</p>&

34、lt;p>　　沙烏卡特 ; 邁克爾</p><p><b>　　摘要</b></p><p>　　風險管理對于最優(yōu)投資組合管理來說是至關重要的。在金融領域里，增長最快的一個就是金融衍品的擴張。風險管理和金融衍生工具，關于這一特殊問題的目的就是要強調某些領域中新穎的計量經(jīng)濟學、金融計量經(jīng)濟學、實證金融風險管理等方法為風險管理分析做出了巨大的貢獻，并且著重強調了金融

35、衍生品，具體條件相關性以及原油和股票指數(shù)的回報之間的波動溢出效應。使用非標準金融產(chǎn)品期權定價，股指期貨將會有500標準普爾的上升或下降，使用馬爾可夫切換多重分形模型來對其波動性進行預測:證據(jù)來自標準普爾100指數(shù)和股票期權，商品交易顧問的業(yè)績表現(xiàn):使用的是一個均值方差比率測試方法，通過股票收益、浮動、交易額和溢出效應來預測其波動性：以巴西為案例，風險或價格持續(xù)時間的預測和威布爾模擬模型:應用ACD模型，預估雙重交易風險，股票和信用違約行

36、業(yè)指數(shù)。動態(tài)模型和風險對沖，在跨國企業(yè)中的風險溢價，解決索賠問題，跌價風險管理和基于風險價值的貴金屬、石油和股票最佳投資組合。</p><p>　　關鍵詞: 風險管理；優(yōu)化組合；金融衍生工具；金融計量經(jīng)濟學；期貨；波動；溢出效應；套期保值；違約；風險溢價;</p><p><b>　　1引言</b></p><p>　　風險管理對最優(yōu)投資組合管

37、理來說是至關重要的。金融領域增長最快的一個就是金融衍生品的擴張。對潛在的風險和投資組合管理的一些關鍵問題都相當了解,而對潛在的金融衍生品的很多技術和經(jīng)驗問題則不是太清楚。</p><p>　　對于這個特殊問題研究，我們希望，那些有意義的、富有價值的和具有創(chuàng)新性的研究性論文將會鼓勵更多的有識之士對風險管理和金融衍生品領域進行各種研究。</p><p><b>　　2 文獻回顧<

38、;/b></p><p>　　第一篇文章,“原油和股票指數(shù)回報之間的條件相關性和波動溢出效應”,作者為：張嘉琳(國立中興大學、臺灣),邁克爾 (伊拉斯姆斯大學鹿特丹，荷蘭)和羅恩(湄州大學，泰國)，他們基于原油回報和股票指數(shù)的回報，調查了原油和金融市場之間的條件相關性和波動溢出效應。</p><p>　　從1998年的1月2日 到2009年的11月4日的原油的日回報率，西德州中級原油

39、和布倫特原油市場的未來期貨價格，富時指數(shù)，紐交所，道瓊斯和標準普爾指數(shù)回報，這些分析都是基于波勒斯勒夫的CCC模型，凌和麥卡利爾 的VARMA-GARCH模型，麥卡利爾，霍蒂和陳的VARMA-AGARCH模型以及恩格爾DCC模型。</p><p>　　基于CCC模型，估計市場回報率的條件相關性非常低，還有一些不具有統(tǒng)計意義，這意味著只有在同一市場條件下才會相關，而不是整個市場。然而，DCC模型預估條件相關性總是很

40、重要的。根據(jù)這個結果，可知假設條件不變，是支持不了相關性實證研究的。</p><p>　　令人驚訝的是，根據(jù)VARMA-GARCH和VARMA-AGARCH模型的實證研究結果提供的證據(jù)，表明原油和金融市場之間的波動溢出效應有一定的相關性。非對稱效應的證據(jù)，表明VARMA-AGARCH模型優(yōu)于VARMA-GARCH和CCC模型。原油的回報和股票指數(shù)的回報之間的相關性的波動率的估計和分析，可以為投資者，石油交易商和政

41、府機構提供有用的信息。他們都關心原油和股票市場，特別是關于兩個市場的最優(yōu)套期保值。</p><p>　　柯恩拉德(南非金山大學)和特蕾莎(南非金山大學)的文章，分析了奇異期權定價的理論和實踐問題。直觀的定價方法是基于幾何布朗運動模型。作者展示了哈馬達和雪利的方法，可用于對一些奇異期權進行定價。通過使用王的轉換模型方法，作者可以檢查價格浮動的范圍。</p><p>　　張嘉琳(國立中興大學、

42、臺灣),邁克爾 (伊拉斯姆斯大學鹿特丹，荷蘭)和佩雷斯(西班牙馬德里大學)，他們分析了“期貨的標準普爾的起伏方差”，建模、監(jiān)測和預測波動性對于明智的投資組合風險管理來說是不可或缺的。</p><p>　　資產(chǎn)綜合指數(shù)的波動率可以通過使用波動衍生工具來獲得，如:波動率和方差互換、期權和期貨。最受歡迎的波動率指數(shù)是VIX指數(shù)，這是一個衡量市場預期的波動的關鍵指標，也因此成為投資者情緒和市場波動的一個重要晴雨表。投資者

43、解釋VIX現(xiàn)金指數(shù)，是一個“恐懼”指數(shù)，以及VIX期權和VIX期貨都是 “恐懼”指數(shù)的衍生品。</p><p>　　文章“使用馬爾可夫切換多重分形模型預測波動性:證據(jù)來自標準普爾100指數(shù)和股票期權”，作者為張文 (國立臺灣大學、臺灣),黃騰(國立臺灣科學和技術大學,臺灣)和林炳輝(國立中興大學，臺灣)，他們評估了馬爾可夫轉折多重分形模型的性能，他們認為，對歷史波動率的預測可以通過波動S&P100指數(shù)和股

44、票期權來實現(xiàn)。</p><p>　　首先，他們發(fā)現(xiàn)MSM和GARCH模型可以預測隱含波動率，效果好于歷史波動率指數(shù)和股票期權。第二，股票期權波動率比索引指標更難以預測到波動率。第三，指數(shù)和股票期權波動率可以更好地預測非全局的金融危機時間段和全球金融危機時間段。第四，股票期權波動率表現(xiàn)出不同的模式，對各種股票和期權的特點和條件的預測取決于這些特征。最后，作者發(fā)現(xiàn)，在不同的股票和期權特征情況下，在預測股票期權波動率方

45、面，MSM模型預測的波動率優(yōu)于隱含波動率。 </p><p><b>　　3 結語</b></p><p>　　對于這個特殊問題研究，我們希望收集關于“風險管理和金融衍生品”領域的一些權威專家的那些有意義的、富有價值的和具有創(chuàng)新性的研究性論文，并且這些研究將會廣泛地推動理論家和實踐者都對風險和投資組合管理、實證金融和金融計量經(jīng)濟學等進行研究，將鼓勵更多的人研究風險管理