風(fēng)險(xiǎn)管理和金融衍生品外文文獻(xiàn)翻譯（節(jié)選）

1、<p>　　1200單詞，7200英文字符,2000漢字</p><p>　　文獻(xiàn)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(xiàn) 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(fēng)險(xiǎn)管理和金融衍生品:一個(gè)綜述</p>&

34、lt;p>　　沙烏卡特 ; 邁克爾</p><p><b>　　摘要</b></p><p>　　風(fēng)險(xiǎn)管理對(duì)于最優(yōu)投資組合管理來(lái)說(shuō)是至關(guān)重要的。在金融領(lǐng)域里，增長(zhǎng)最快的一個(gè)就是金融衍品的擴(kuò)張。風(fēng)險(xiǎn)管理和金融衍生工具，關(guān)于這一特殊問(wèn)題的目的就是要強(qiáng)調(diào)某些領(lǐng)域中新穎的計(jì)量經(jīng)濟(jì)學(xué)、金融計(jì)量經(jīng)濟(jì)學(xué)、實(shí)證金融風(fēng)險(xiǎn)管理等方法為風(fēng)險(xiǎn)管理分析做出了巨大的貢獻(xiàn)，并且著重強(qiáng)調(diào)了金融

35、衍生品，具體條件相關(guān)性以及原油和股票指數(shù)的回報(bào)之間的波動(dòng)溢出效應(yīng)。使用非標(biāo)準(zhǔn)金融產(chǎn)品期權(quán)定價(jià)，股指期貨將會(huì)有500標(biāo)準(zhǔn)普爾的上升或下降，使用馬爾可夫切換多重分形模型來(lái)對(duì)其波動(dòng)性進(jìn)行預(yù)測(cè):證據(jù)來(lái)自標(biāo)準(zhǔn)普爾100指數(shù)和股票期權(quán)，商品交易顧問(wèn)的業(yè)績(jī)表現(xiàn):使用的是一個(gè)均值方差比率測(cè)試方法，通過(guò)股票收益、浮動(dòng)、交易額和溢出效應(yīng)來(lái)預(yù)測(cè)其波動(dòng)性：以巴西為案例，風(fēng)險(xiǎn)或價(jià)格持續(xù)時(shí)間的預(yù)測(cè)和威布爾模擬模型:應(yīng)用ACD模型，預(yù)估雙重交易風(fēng)險(xiǎn)，股票和信用違約行

36、業(yè)指數(shù)。動(dòng)態(tài)模型和風(fēng)險(xiǎn)對(duì)沖，在跨國(guó)企業(yè)中的風(fēng)險(xiǎn)溢價(jià)，解決索賠問(wèn)題，跌價(jià)風(fēng)險(xiǎn)管理和基于風(fēng)險(xiǎn)價(jià)值的貴金屬、石油和股票最佳投資組合。</p><p>　　關(guān)鍵詞: 風(fēng)險(xiǎn)管理；優(yōu)化組合；金融衍生工具；金融計(jì)量經(jīng)濟(jì)學(xué)；期貨；波動(dòng)；溢出效應(yīng)；套期保值；違約；風(fēng)險(xiǎn)溢價(jià);</p><p><b>　　1引言</b></p><p>　　風(fēng)險(xiǎn)管理對(duì)最優(yōu)投資組合管

37、理來(lái)說(shuō)是至關(guān)重要的。金融領(lǐng)域增長(zhǎng)最快的一個(gè)就是金融衍生品的擴(kuò)張。對(duì)潛在的風(fēng)險(xiǎn)和投資組合管理的一些關(guān)鍵問(wèn)題都相當(dāng)了解,而對(duì)潛在的金融衍生品的很多技術(shù)和經(jīng)驗(yàn)問(wèn)題則不是太清楚。</p><p>　　對(duì)于這個(gè)特殊問(wèn)題研究，我們希望，那些有意義的、富有價(jià)值的和具有創(chuàng)新性的研究性論文將會(huì)鼓勵(lì)更多的有識(shí)之士對(duì)風(fēng)險(xiǎn)管理和金融衍生品領(lǐng)域進(jìn)行各種研究。</p><p><b>　　2 文獻(xiàn)回顧<

38、;/b></p><p>　　第一篇文章,“原油和股票指數(shù)回報(bào)之間的條件相關(guān)性和波動(dòng)溢出效應(yīng)”,作者為：張嘉琳(國(guó)立中興大學(xué)、臺(tái)灣),邁克爾 (伊拉斯姆斯大學(xué)鹿特丹，荷蘭)和羅恩(湄州大學(xué)，泰國(guó))，他們基于原油回報(bào)和股票指數(shù)的回報(bào)，調(diào)查了原油和金融市場(chǎng)之間的條件相關(guān)性和波動(dòng)溢出效應(yīng)。</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(bào)率，西德州中級(jí)原油

39、和布倫特原油市場(chǎng)的未來(lái)期貨價(jià)格，富時(shí)指數(shù)，紐交所，道瓊斯和標(biāo)準(zhǔn)普爾指數(shù)回報(bào)，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(jì)市場(chǎng)回報(bào)率的條件相關(guān)性非常低，還有一些不具有統(tǒng)計(jì)意義，這意味著只有在同一市場(chǎng)條件下才會(huì)相關(guān)，而不是整個(gè)市場(chǎng)。然而，DCC模型預(yù)估條件相關(guān)性總是很

40、重要的。根據(jù)這個(gè)結(jié)果，可知假設(shè)條件不變，是支持不了相關(guān)性實(shí)證研究的。</p><p>　　令人驚訝的是，根據(jù)VARMA-GARCH和VARMA-AGARCH模型的實(shí)證研究結(jié)果提供的證據(jù)，表明原油和金融市場(chǎng)之間的波動(dòng)溢出效應(yīng)有一定的相關(guān)性。非對(duì)稱效應(yīng)的證據(jù)，表明VARMA-AGARCH模型優(yōu)于VARMA-GARCH和CCC模型。原油的回報(bào)和股票指數(shù)的回報(bào)之間的相關(guān)性的波動(dòng)率的估計(jì)和分析，可以為投資者，石油交易商和政

41、府機(jī)構(gòu)提供有用的信息。他們都關(guān)心原油和股票市場(chǎng)，特別是關(guān)于兩個(gè)市場(chǎng)的最優(yōu)套期保值。</p><p>　　柯恩拉德(南非金山大學(xué))和特蕾莎(南非金山大學(xué))的文章，分析了奇異期權(quán)定價(jià)的理論和實(shí)踐問(wèn)題。直觀的定價(jià)方法是基于幾何布朗運(yùn)動(dòng)模型。作者展示了哈馬達(dá)和雪利的方法，可用于對(duì)一些奇異期權(quán)進(jìn)行定價(jià)。通過(guò)使用王的轉(zhuǎn)換模型方法，作者可以檢查價(jià)格浮動(dòng)的范圍。</p><p>　　張嘉琳(國(guó)立中興大學(xué)、

42、臺(tái)灣),邁克爾 (伊拉斯姆斯大學(xué)鹿特丹，荷蘭)和佩雷斯(西班牙馬德里大學(xué))，他們分析了“期貨的標(biāo)準(zhǔn)普爾的起伏方差”，建模、監(jiān)測(cè)和預(yù)測(cè)波動(dòng)性對(duì)于明智的投資組合風(fēng)險(xiǎn)管理來(lái)說(shuō)是不可或缺的。</p><p>　　資產(chǎn)綜合指數(shù)的波動(dòng)率可以通過(guò)使用波動(dòng)衍生工具來(lái)獲得，如:波動(dòng)率和方差互換、期權(quán)和期貨。最受歡迎的波動(dòng)率指數(shù)是VIX指數(shù)，這是一個(gè)衡量市場(chǎng)預(yù)期的波動(dòng)的關(guān)鍵指標(biāo)，也因此成為投資者情緒和市場(chǎng)波動(dòng)的一個(gè)重要晴雨表。投資者

43、解釋VIX現(xiàn)金指數(shù)，是一個(gè)“恐懼”指數(shù)，以及VIX期權(quán)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(yù)測(cè)波動(dòng)性:證據(jù)來(lái)自標(biāo)準(zhǔn)普爾100指數(shù)和股票期權(quán)”，作者為張文 (國(guó)立臺(tái)灣大學(xué)、臺(tái)灣),黃騰(國(guó)立臺(tái)灣科學(xué)和技術(shù)大學(xué),臺(tái)灣)和林炳輝(國(guó)立中興大學(xué)，臺(tái)灣)，他們?cè)u(píng)估了馬爾可夫轉(zhuǎn)折多重分形模型的性能，他們認(rèn)為，對(duì)歷史波動(dòng)率的預(yù)測(cè)可以通過(guò)波動(dòng)S&P100指數(shù)和股

44、票期權(quán)來(lái)實(shí)現(xiàn)。</p><p>　　首先，他們發(fā)現(xiàn)MSM和GARCH模型可以預(yù)測(cè)隱含波動(dòng)率，效果好于歷史波動(dòng)率指數(shù)和股票期權(quán)。第二，股票期權(quán)波動(dòng)率比索引指標(biāo)更難以預(yù)測(cè)到波動(dòng)率。第三，指數(shù)和股票期權(quán)波動(dòng)率可以更好地預(yù)測(cè)非全局的金融危機(jī)時(shí)間段和全球金融危機(jī)時(shí)間段。第四，股票期權(quán)波動(dòng)率表現(xiàn)出不同的模式，對(duì)各種股票和期權(quán)的特點(diǎn)和條件的預(yù)測(cè)取決于這些特征。最后，作者發(fā)現(xiàn)，在不同的股票和期權(quán)特征情況下，在預(yù)測(cè)股票期權(quán)波動(dòng)率方

45、面，MSM模型預(yù)測(cè)的波動(dòng)率優(yōu)于隱含波動(dòng)率。 </p><p><b>　　3 結(jié)語(yǔ)</b></p><p>　　對(duì)于這個(gè)特殊問(wèn)題研究，我們希望收集關(guān)于“風(fēng)險(xiǎn)管理和金融衍生品”領(lǐng)域的一些權(quán)威專家的那些有意義的、富有價(jià)值的和具有創(chuàng)新性的研究性論文，并且這些研究將會(huì)廣泛地推動(dòng)理論家和實(shí)踐者都對(duì)風(fēng)險(xiǎn)和投資組合管理、實(shí)證金融和金融計(jì)量經(jīng)濟(jì)學(xué)等進(jìn)行研究，將鼓勵(lì)更多的人研究風(fēng)險(xiǎn)管理