本文使用混合分配方法發展一個可處理厚尾,偏態(報酬分配不對稱)的壓力 測試模型。在資料上,我們以希臘國債與 S&P500 指數作為核心資產,臺灣市場 的標的資產作為邊緣資產。在與資料的配適能力上,本文發展的模型確實優於過 去假設常態分配的壓力測試模型。在實際執行壓力測試中,本研究比較了本文使 用的混合分配模型與過去模型的差異,我們發現壓力測試結果的差異相當大,因 此肯定了能抓住厚尾及偏態現象模型的重要性。

該篇文章中提出一個新的交易方式：條件機率交易模型conditional probability trading model。 這個模型應用了三個主要的基本假設： （1）總體經濟因子和股價指數間有相關性。因此可以透過總經指標來衡量股市應有的合理價位。 （2）股價具有回歸均數的特質。亦即股價一旦過度偏離基本價值，理論上會傾向回復到基本價值之上。 （3）股價指數相對於基本價值線的距離，將會影響偏態係數的大小。 根據以上三個性質，試圖建構出一個能夠捕捉股價指數變動的模型，並用以進行交易模擬，觀察其是否能獲取正報酬。 / The trading strategy, conditional probability trading model(CPTM), is presented in this article. We’ve tried to develop a new trading strategy which is built up by the combination of the properties which includes 1)the relationship between macroeconomic factors and stock market. 2) mean reversion and 3) conditional skewness. The conclusion implies that we may successfully find out a method to combine fundamental and technical analysis, if this method is proved effective. The former hypothesis is assumed that the different level of stock market index may stand for a specific condition of macroeconomic environment. Meanwhile, a better fundamental economic condition could reasonably create a higher stock market index, vice versa. By observing the fundamental value, we can figure out the market ,currently, is over-priced or under-priced. Next, we construct a trading model which is graphed like Bollinger bands. According to specific rules, it provides buying or selling signals. In some special situations, it can also forecast the turning points of the stock market precisely. 3) Skewness also plays a very important role in CPTM, because one of the hypothesis assumes that overpriced /underpriced stock market probably accompanies with left-skewed / right-skewed distribution of daily stock return. The hypothesis of dynamically adjusted skewness implies the concept that over-priced/under-priced stock market has higher propensity to decline/rise. To judge the trading timing is the core value in this model.

此論文研究有什麼因子會影響台灣股票加權指數報酬率之偏態係數。過去的文獻顯示，交易量和報酬率為可能的因子。實證的結果確實發現，交易量和報酬率顯著地影響偏態係數。 / This study examines the determinants for conditional skewness of the return distribution of the Taiwan Stock Exchange Value-Weighted Index. Important driving factors that affect conditional skewness, based on the theory literature, include trading volumes and returns. To capture the skewness in the data, the family of time series model we consider focuses on the specifications of higher-order moments than mean and volatility that conventional models look at. With the specifications, we are able to test whether the factors, volumes and returns, can influence conditional skewnees of the return distribution. Our results suggest the significance of the factors using data from the Taiwan Stock Exchange Value-Weighted Index.

市場上通常存在著跌幅大過漲幅的現象，更強烈的說法是，市場會在一夕之間崩盤，但卻不會在一夕之間漲上天，這造成了報酬負偏態的現象，而Horn and Stein的理論模型認為市場存在著兩群堅持己見、對股價有不同看法的投資人，再加上這群投資人面對放空的限制，是造成報酬負偏態的主要因素，若投資人之間看法差異愈大，則負偏態現象愈明顯。Chen, Horn and Stein根據他們的理論模型，他們將成交量定義週轉率，提出利用股票的週轉率來預測負偏態的概念，而本研究利用他們所提出的實證模型，應用在台灣股市上，並與美國實證結果相對照，實證結果顯示： 1. 在台灣，6個月期間週轉率愈高於平均的個股或大盤，下6個月報酬負偏態的情況會愈顯著，但其影響力和美國實證結果相對照小很多。 2. 市值愈大的股票，其報酬正偏態的情況愈顯著，這與美國的實證結果是相反的。 3. 依隨機泡沫模型理論，過去報酬率愈大的資產，愈有可能產生報酬負偏態的情況，而台灣的實證顯示，過去的報酬率無法有效的預測報酬負偏態，但美國的實證結果是成功的 / In stock market history, the very large movement are always decrease rather than increase. In other words, stock market tends to melt down, not melt up. This kind of return asymmetry causes the negative skewness of the stock return (either market portfolio or single stock). There are mainly three schools to explain mechanism behind the negative skewness of the return. They are leverage effect, assymmetry volatility, and stochastic bubble model. Chen, Horn and Stein states that stocks come through high turnover will later on go through the negative skewness of return. We use the empirical model proposed by Horn and Stein to inpsect if turnover can predict negative skewness of return in Taiwan stock market. we have three conclusions： 1. Negative skewness is greater in stocks and market portfolio that have experienced an increase in turnover rate relative to trend over the prior six month. This effect is smaller than that in America. 2. Negative skewness is greater in stocks that are larger in terms of market capitalization. This empirical evidence is contrary to those in America. 3. In view of stochastic bubble model, stocks that have high positive returns in the past are more likely to experience greater negative skewness in return. Empirical evidence in Taiwan shows that stochastic bubble does not apply to Taiwan stocks market, that is, past return in stocks can not predict the negative skewness in return.

自Markowitz(1952)提出平均數-變異數準則以來，對於該準則適宜性的討論即不曾停止過。許多實證上資料顯示資產報酬率分配不為常態，而越來越多學者也對於高於二階以上之高階動差對投資決策之影響提出證實。本文利用臺灣八大類股指數報酬率分配資料，運用多目標規劃求解法進行實證，發現臺灣股票市場呈現顯著峰態性質，此外，本文樣本外試驗結果亦指出，平均數-變異數-偏態-峰態架構下之最適投資組合的報酬率高於傳統平均數-變異數架構下之最適投資組合以及大盤報酬。

2015年下半年開始，許多有關市場黑天鵝的新聞佈滿各大媒體版面，其中不乏「某恐慌指標創歷史新高」此類令投資人恐懼的標題。然事實上卻未見到各國股價指數有大幅修正的現象，以MSCI全球指數而言，下半年總計僅修正6.49%。為了探討這些不同於傳統VIX指數的恐慌指標是否會顯著影響股價指數的表現。本論文透過VAR、VECM以及ARDL模型，探討金價油價比、CBOE偏態指數、瑞士信貸CSFB指數以及泰德價差這四種恐慌指標對於當前全球前四大經濟體股價指數的關聯性。 美國是全世界經濟的領頭羊，其經濟情勢與全球每一個國家的榮景息息相關，美國股價指數的表現亦是相當受到全球投資人所關注的。故本論文首先透過探討這四種恐慌指標對於S&P 500指數的影響，再利用S&P 500指數領先各國股價指數的特性進一步得出結論。實證結果發現，S&P 500指數對於其他三個股價指數確實具有短期同向的影響，長期而言亦具有穩定的線性關係。另外，金價油價比無論在短期及長期下皆無法有效代理市場的恐慌程度而影響S&P 500指數；CBOE偏態指數與瑞士信貸CSFB指數在長期下得以領先S&P 500指數的變化，當該二指數走高，代表 S&P 500指數在近期的波段高點可能即將來臨，亦即隱含該二指數對於S&P 500指數具有領先同向變化的現象；泰德價差為市場用以衡量信用風險的指標之一，當泰德價差擴大，隱含市場風險貼水增加，不利股市發展，其與S&P 500指數則具有長期穩定的負向關係。本論文最後也針對這四種恐慌指標的預測能力進行探討，發現瑞士信貸CSFB指數在預測S&P 500指數的能力上，相對其他三種恐慌指標優異。 / There were so many hearsays about the potential black swan events dominating the news in the second half of 2015. Headlines were about some fear indicators hit historic high but, in realistic, world stock market did not be significantly influenced under this panic atmosphere. Take MSCI World Index for instance, the index dropped only 6.49% in the second half of 2015, which was relatively unreasonable under this condition. In order to find out whether or not the fluctuations of these fear indicators can significantly affect stock indexes, VAR, VAEM and ARDL model to discuss the relationships between 4 fear indicators and 4 stock indexes─gold to oil ratio, CBOE Skew Index, Credit Suisse Fear Barometer Index, TED spread, S&P 500 Index, MSCI Europe Index, SSE A Share Index and Nikkei 225 Index are adopted in this study. Global investors pay close attention to the performance of the U.S. Stock indexes as U.S. economy condition can affect the economies of the rest of the world. Consequently, we investigated the effects of 4 fear indicators to the S&P 500 Index then employed relationships between S&P 500 Index and other 3 stock indexes to do further discussion. The results show S&P 500 positively affects the performances of other 3 stock indexes in short term and has a steady relationship with each of them respectively in the long term. The changes of gold to oil ratio could not significantly influence the performance of S&P 500 Index no matter in the short term or the long term. CBOE Skew Index and CSFB Index have significant positive influences on S&P 500 and are leading indicators to S&P 500 Index. Lastly, TED spread has a steady negative relationship with S&P 500 in long term, and CSFB Index has the highest predictive power among the 4 fear indicators.

本論文共分三章，全數圍繞在消費尤拉方程式中，消費成長的高階動差在理論與實證上的重要性。分別說明如下： 本論文第一章討論消費高階動差在實證估計消費結構性參數之重要性。消費尤拉方程式是消費者極大化問題的一階條件，而自Hall (1978)起，估計消費結構參數如跨期替代彈性時，也多是利用這個尤拉方程式所隱涵的消費動態關係，進行估計。但是由於消費資料存在嚴重的衡量誤差問題，實證上多將尤拉方程式進行對數線性化，或是二階線性化後進行估計。 然而前述一、二階線性化，固然處理了資料的衡量誤差問題，卻也造成了參數估計上的近似誤差(approximation bias)。其原因來自於線性化過程中所忽略的高階動差實為內生，而與迴歸式中的二階動差相關。這使得即便用工具變數進行估計，仍然無法產生具有一致性的估計結果。這當中的原因在於足以解釋二階動差，卻又與殘差項中的高階動差直交的良好(valid)的工具變數無法取得。 我們認為在資料普遍存在衡量誤差的情況下，線性化估計尤拉方程式不失為一可行又易於操作的方法。於是我們嘗試在線性化的尤拉方程式中，將高階動差引入，並檢視這種高階近似是否能有效降低近似誤差。我們的模擬結果首先證實，過去二階近似尤拉方程式的估計，確實存在嚴重近似誤差。利用工具變數雖然可以少部份降低該誤差，但由於高階動差的內生性質，誤差仍然顯著。我們也發現，將高階動差引入模型，確實可以大幅降低近似誤差，但是在偏誤降低的同時，參數估計效率卻也隨之降低。 高階動差的引入，除了降低近似偏誤外，卻也必須付出估計效率降低的代價。我們因此並不建議無限制地放入高階動差。則近似階次選取，乃為攸關估計績效的重要因素。本章的第二部份，即著眼於該最適近似階次選取。我們首先定義使參數估計均方誤(mean squared error, MSE)為最小的近似階次，為最適近似階次。我們發現，該最適階次與樣本大小、效用函數的彎曲程度都有直接的關係。 然而在實際進行估計時，由於參數真值無法得知，MSE準則自然無法作為階次選取之依據。我們於是利用目前在模型與階次選取上經常被使用的一些準則進行階次選取，並比較這些不同準則下參數估計的MSE。我們發現利用這些準則，確實可以使高階近似尤拉方程式得到MSE遠低於目前被普遍採用的二階近似的估計結果，而為估計消費結構參數時更佳的選擇。 本論文第二章延續前一章的模擬結果，嘗試利用消費高階動差間的非線性關係，進一步改善高階近似消費尤拉方程式的估計表現。由第一章的研究結果，我們發現高階近似估計確有助大幅降低近似誤差，但這其中可能產生的估計效率喪失，卻是輕乎不得的。這個效率喪失，很大一部份來自於我們所使用的工具變數，雖然可以有效掌握消費成長二階動差的變動，但是當這同一組工具變數被用來解釋如偏態與峰態等這些更高階動差時，預測力卻大幅滑落。這使待得當我們將這些配適度偏低的配適後高階動差，放到迴歸式中進行估計時，所能提供的額外情報也就相當有限。而所造成的共線性問題，也自然使得估計效率大幅惡化。 於是在其他合格的工具變數相對有限的情況下，我們利用高階動差間所存在的均衡關係，將原來的工具變數進行非線性轉換，以求得對高階動差的較佳配適。由於消費動差間之關係，尚未見諸相關文獻。於是我們首先透過數值分析，進一步釐清消費高階動差間之關係。這其中尤為重要的是由消費二階動差所衡量的消費風險，與更高階動差間之關係。因為這些關係將為我們轉換工具變數之依據。 我們發現與二階動差相一致地，消費者對這些高階動差之預期，都隨其財富水準的提高而減少。這隱涵消費風險與更高階動差間之正向關係。更進一步檢視消費風險與高階動差間之關係也發現，二者間確實存在非線性之正向關係。而這也解釋了何以前一章線性的工具變數，雖可適切捕捉消費風險，但對高階動差的解釋力卻異常薄弱。 利用這些非線性關係，我們將原始的工具變數進行非線性轉換後，用以配適更高階動差。透過模擬分析，我們證實了這些非線性工具變數，確實大幅改善高階近似尤拉方程式的估計表現。除了仍保有與線性工具變數般的一些特性，諸如隨樣本的增加，最適近似階次也隨之增加之外，相較於線性工具變數，非線性工具變數可以在較低的近似階次下，就使得估計偏誤大幅下降。在近似階次愈高估計效率愈低的情況下，這自然大幅度地提高了估計效率。比較兩種工具變數估計結構數參數所產生的MSE也證實，非線性工具變數確實有遠低於原始線性工具變數的MSE表現。 然而我們同時也發現，利用非線性工具變數估計，若未適當選擇近似階次，效率喪失的速度，可能更甚於線性工具變數時。這凸顯了選擇近似階次的重要性。於是我們同樣檢視了前述階次選擇準則在目前非線性工具變數環境下的適用性。而總結第一、二章的研究結果，我們凸顯了高階動差的重要性，確實助益重要消費結構參數估計。而利用過去尚未被討論過的高階動差間非線性關係，更可大幅度改善估計績效。 本論文的最後一章，則旨在理論上建立高階動差的重要性。我們在二次式的效用函數(quadratic utility function)設定下，推導借貸限制下的最適消費決策。二次式的效用函數，由於其邊際價值函數(marginal value function)為一線性函數，因此所隱涵的消費決策，具有確定相等(certainty equivalence)的特性。這表示消費者只關心未來的期望消費水準，二階以上的更高階動差，都不影響其消費決策。然而這種確定相等的特性，將因為借貸限制的存在而不復存在，而高階動差的重要性也就因此凸顯。 我們證明，確定相等特性的喪失，其背後的理論原因在於，借貸限制的存在，使得二次式效用函數的邊際價值函數，產生凸性。消費者因而因應未來的不確定性，進行預防性儲蓄。透過分析解的求得，我們也得以進一步分析更高階動差的對消費決策的理論性質。同時我們也引申理論推導的實證意涵，其中較重要者諸如未受限消費者因預防性儲蓄行為所引發的消費過度敏感性現象，實證上樣本分割法的選取，以及高階動差的引入模型。 / The theme of this thesis seeks to explore the importance of higher-order moments in the consumption Euler equation, both theoretically and empirically. Applying log-linearized versions of Euler equations has been a dominant approach to obtaining sensible analytical solutions, and a popular choice of model specifications for estimation. The literature however by now has been no lack of conflicting empirical results that are attributed to the use of the specific version of Euler equations. Important yet natural questions whether the higher-order moments can be safely ignored, or whether higher-order approximations offer explanations to the stylized facts remain unanswered. Such inquires as in the thesis thus can improve our understanding toward consumer behaviors over prior studies based on the linear approximation. 1. What Do We Gain from Estimating Euler Equations with Higher-Order Approximations? Despite the importance of estimating structural parameters governing consumption dynamics, such as the elasticity of intertemporal substitution, empirical attempts to unveil these parameters using a log-linearized version of the Euler equation have produced many puzzling results. Some studies show that the approximation bias may well constitute a compelling explanation. Even so, the approximation technique continues to be useful and convenient in estimation of the parameters, because noisy consumption data renders a full-fledged GMM estimation unreliable. Motivated by its potential success in reducing the bias, we investigate the economic significance and empirical relevance of higher-order approximations to the Euler equation with simulation methodology. The higher-order approximations suggest a linear relationship between expected consumption growth and its higher-order moments. Our simulation results clearly reveal that the approximation bias can be significantly reduced when the higher-order moments are introduced into estimation, but at the cost of efficiency loss. It therefore documents a clear tradeoff between approximation bias reduction and efficiency loss in the consumption growth regression when higher-order approximations to the Euler equation is considered. A question of immediate practical interest arises ``How many higher-order terms are needed?'' The second part of our Monte-Carlo studies then deals with this issue. We judge whether a particular consumption moment should be included in the regression by the criterion of mean squared errors (MSE) that accounts for a trade-off between estimation bias and efficiency loss. The included moments leading to smaller MSE are regarded as ones to be needed. We also investigate the usefulness of the model and/or moment selection criteria in providing guidance in selecting the approximation order. We find that improvements over the second-order approximated Euler equation can always be achieved simply by allowing for the higher-order moments in the consumption regression, with the approximation order selected by these criteria. 2. Uncovering Preference Parameters with the Utilization of Relations between Higher-Order Consumption Moments Our previous attempt to deliver more desirable estimation performance with higher-order approximations to the consumption Euler equation reveals that the approximation bias can be significantly reduced when the higher-order moments are introduced into estimation, but at the cost of efficiency loss. The latter results from the difficulty in identifying independent variation in the higher-order moments by sets of linear instruments used to identify that in variability in consumption growth, mainly consisting of individual-specific characteristics. Thus, one major challenge in the study is how to obtain quality instruments that are capable of doing so. With the numerical analysis technique, we first establish the nonlinear equilibrium relation between consumption risk and higher-order consumption moments. This nonlinear relation is then utilized to form quality instruments that can better capture variations in higher-order moments. A novelty of this chapter lies in adopting a set of nonlinear instruments that is to cope with this issue. They are very simple moment transformations of the characteristic-related instruments, thereby easy to obtain in practice. As expected, our simulations demonstrate that for a comparable amount of the bias corrected, applying the nonlinear instruments does entail an inclusion of fewer higher-order moments in estimation. A smaller simulated MSE that reveals the improvement over our previous estimation results can thus be achieved.\ 3. Precautionary Saving and Consumption with Borrowing Constraint This last chapter offers a theoretical underpinning for the importance of the higher-order moments in a simple environment where economic agents have a quadratic-utility preference. The resulting Euler equation gives rise to a linear policy function in essence, or a random-walk consumption rule. The twist in our theory comes from a presence of borrowing constraint facing consumers. The analysis shows that the presence of the constraint induces precautionary motives for saving as responses from consumers to income uncertainties, even there has been no such motives inherent in consumers' preference. The corresponding value function now displays a convexity property that is virtually only associated with more general preferences than a quadratic utility. The analytical framework allows us to be able to characterize saving behaviors that are of precautionary motives, and their responses to changes in different moments of income process. As empirical implications, our analysis shed new light on the causes of excess sensitivity, the consequences of sample splitting between the rich and the poor, as well as the relevance of the higher-order moments to consumption dynamics, specifically skewness and kurtosis.

隨著世界金融的改革開放，國際匯率、利率、股票、債券以及相關衍生性金融商品的突破創新，為企業提供充裕且分散的資金管道，但亦對於企業風險之控管投下一顆不定時的炸彈。基於風險控管的必要性，風險值(VaR)技術與觀念，也就應運而生。VaR可以明確量化風險大小為絕對金額，即使不同的金融商品也可利用其相關性加以整合，因此匯率、利率、股票及各式衍生性金融商品的投資組合皆可用整合的技巧算出。 　本研究利用歷史模擬、Bootstrap、Delta-Normal、Gamma、Hull & White混合常態、Cronish-Fisher偏峰態修正、Barone 整合法(Unified)等模型，分別計算股票、外匯、債券、及權證等個別資產投組，再運用Basle提出的回溯測試(Back Test)與前向測試(Forward Test)、Kupiec的LR test、Hendricks提出的評比方法以及Lopez提出的驗證方法，共十二種測定指標，進行各投資組合VaR模型優劣之區分。 　最後再運用較優之VaR模型估計與驗證同時持有股票、外匯、權証三種資產投資組合，以及股票、外匯、權証、債券等四種資產投資組合的總投資組合VaR，尋求最適切、簡易且不失精確的模型，在考慮各種資產間相關性下，統合計算所持有之多元化金融資產較精確、客觀的風險值。 　本研究結論如下： 一、 認購權証資產屬於右偏，即負報酬機率較高，使用CronishFisher偏態修正模型，可以得到較適切估計值；但其他資產有時準確有時不準確。 　二、 台灣認購權証市場，隱含波動度往往大於歷史波動度1至3倍，且用隱含波動度所求算的VaR驗證結果不佳，但利用歷史波動度實證結果佳。 　三、 Hull&White混合常態轉換模型在外匯資產上表現較股票資產精確，這可能受到股票投組報酬率分配較外匯投組具不確定性的影響。 　四、 債券資產投組，隨著持有存續期間越長，債券價格報酬率標準差越高，則債券投組的持有風險也隨之增加，亦即VaR估計值會趨於保守。 　五、 使用Delta-Gamma法，估計非線性資產（認購權証、債券）之VaR與Delta- Normal模型驗證結果相近，故吾人在估計一天之VaR，不需考慮二階風險。 　六、 不同VaR模型受到不同之資產特性、最適衰退因子、信心水準假設、歷史窗口長度等因素影響，導致各VaR模型準確性的差異，本研究選取單資產投組中較佳且易擴充於多資產，考慮相關性的Unified（整合）模型進行多資產投組VaR的估計與驗證，其驗證結果易優。 　七、 在多資產投組VaR的估計上，應考量資產間的相關性，根據證實Unified(div)考慮相關模型表現較未考慮相關模型Unified(undiv)佳。

本研究探討VIX 期貨價格所隱含的資訊對於S&P 500 指數波動度預測的解釋力。過去許多文獻主要運用線性預測模型探討歷史波動度、隱含波動度和風險中立偏態對於波動度預測的資訊內涵。然而過去研究顯示，波動度具有長期記憶與非線性的特性，因此本文主要研究非線性預測模型對於波動度預測的有效性。本篇論文特別著重在不同市場狀態下(高波動與低波動)的實現波動度及隱含波動度異質自我迴歸模型(HAR-RV-IV model)。因此，本研究以考慮馬可夫狀態轉化下的異質自我迴歸模型(MRS-HAR model)進行實證分析。 本研究主要目的有以下三點: (1) 以VIX期貨價格所隱含的資訊提升S&P 500波動度預測的準確性。(2) 結合風險中立偏態與VIX期貨的資訊內涵，進一步提升S&P 500 波動度預測的準確性。(3) 考慮狀態轉換後的波動度預測模型是否優於過去文獻的線性迴歸模型。 本研究實證結果發現: (1) 相對於過去的實現波動度及隱含波動度，VIX 期貨可以提供對於預測未來波動度的額外資訊。 (2) 與其他模型比較，加入風險中立偏態和VIX 期貨萃取出的隱含波動度之波動度預測模型，只顯著提高預測未來一天波動度的準確性。 (3) 考慮狀態轉換後的波動度預測模型優於線性迴歸模型。 / This paper explores whether the information implied from VIX futures prices has incremental explanatory power for future volatility in the S&P 500 index. Most of prior studies adopt linear forecasting models to investigate the usefulness of historical volatility, implied volatility and risk-neutral skewness for volatility forecasting. However, previous literatures find out the long-memory and nonlinear property in volatility. Therefore, this study focuses on the nonlinear forecasting models to examine the effectiveness for volatility forecasting. In particular, we concentrate on Heterogeneous Autoregressive model of Realized Volatility and Implied Volatility (HAR-RV-IV) under different market conditions (i.e., high and low volatility state). This study has three main goals: First, to investigate whether the information extracted from VIX futures prices could improve the accuracy for future volatility forecasting. Second, combining the information content of risk-neutral skewness and VIX futures to enhance the predictive power for future volatility forecasting. Last, to explore whether the nonlinear models are superior to the linear models. This study finds that VIX futures prices contain additional information for future volatility, relative to past realized volatilities and implied volatility. Out-of-sample analysis confirms that VIX futures improves significantly the accuracy for future volatility forecasting. However, the improvement in the accuracy of volatility forecasts is significant only at daily forecast horizon after incorporating the information of risk-neutral skewness and VIX futures prices into the volatility forecasting model. Last, the volatility forecasting models are superior after taking the regime-switching into account.

因為股票市場常具有厚尾、偏態和峰態的特性且在國際的股票市場之間，股票報酬長存在有尾端相依的情況，所以我們的資產模型不能選用Gaussian分配。 近幾年來，常用GH 分配建構單維度的股票報酬。這篇文章將利用多元仿射JD、多元仿射VG 和多元仿射NIG分配去建構風險性資產的報酬並請應用到資產配置。 建構風險性資產的報酬後，我們提供兩種不同形式的投資組合並且可以導出投資組合的期望值、變異數、偏態和峰態。我們嘗試以投資組合的期望值、變異數、偏態和峰態當成我們的目標函數，然後得出未來最佳的投資組合的權重。為了讓我們的資產配置更加動態和有效率，我們重新估計模型的參數、選擇最佳的投資組合權重，然後重新評估最佳的資產配置在每個決策日期。實證結果發現當股票市場的表現好的時候，我們建議資產配置應使用偏態當成我們的目標函數，但是當股票市場的表現太好的時候，我們建議資產配置應使用變異數當成我們的目標函數。 / Since the stock markets always have the characteristics of heavy-tailness, skewness and kurtosis and there exists tail dependence among the international stock markets, we can’t use the Gaussian distribution as our model. Recently, the generalized hyperbolic (GH) distribution has been suggested to fit the single stock returns. This article will use the multivariate affine JD (MAJD), multivariate affine variance gamma (MAVG) and multivariate affine normal inverse Gaussian (MANIG) distributions to construct the risky asset returns, and apply them to asset allocation. After constructing the risky asset returns, we provide two different forms of portfolio and obtain the mean, variance, skewness, kurtosis of portfolio. We can try to select the optimal weights of portfolio by using the mean, variance, skewness, kurtosis of portfolios as our objective functions. To make our asset allocation more dynamic and efficient, we re-estimate all parameters for our models, select the optimal weights of portfolio, and re-assess the optimal asset allocation at each decision date. Empirically, when the performances of stock markets are good, we suggest that our asset allocation uses the skewness as the objective function. When the performances of stock markets are not good, we suggest that our asset allocation uses the variance as the objective function.