Volatilitas harga yang ditransmisikan dari suatu harga ke harga lain, kemungkinan besar terjadi karena adanya transmisi harga antara kedua pasar tersebut. Hal ini terutama disebabkan karena semakin terbukanya pasar akibat adanya globalisasi dan libelarisasi pedagangan sehingga membuat perekonomian setiap negara terintegrasi secara global. Dengan demikian akan semakin memperlebar kecenderungan terjadinya transmisi harga bahkan volatilityspillover terutama anatara harga CPO dunia dan harga minyak goreng di Indonesia seperti yang telah dibuktikan secara statistik dalam penelitian ini.
saham Cina pada tingkat kepercayaan 95%. Sementara itu, dari tabel 2 dapat kita ketahui bahwa ternyata volatilitas pasar saham Cina tidak dipengaruhi oleh volatilityspillover dari pasar saham Indonesia. Dari pengujian model pertama ini, maka dapat ditarik kesimpulan bahwa terjadi volatilityspillover antara pasar saham Indonesia dengan Cina. Adapun volatilityspillover tersebut sifatnya hanyalah searah (Cina mempengaruhi Indonesia), namun ternyata pasar saham Indonesia tidak mempengaruhi pasar saham Cina. Kemungkinan tidak terjadinya volatilityspillover dari pasar saham Indonesia ke pasar saham Cina adalah dikarenakan investor yang dapat masuk ke pasar saham Cina hanyalah investor lokal, sehingga mungkin mereka cenderung mengabaikan informasi dari luar. Sementara itu, banyak investor asing di pasar saham Indonesia yang biasanya mengambil keputusan berdasarkan informasi yang mereka terima baik itu dari pergerakan/volatilitas pasar saham Amerika Serikat, pasar saham Eropa, dan bahkan pasar saham kawasan regional yang sama.
Free Trade in Indonesia causes import of garlic always increase so domestic price of garlic isn’t entirely influenced by supply and demand of domestic, but it follows import price of garlic. It causes domestic price of garlic is more unpredictable. Therefore, this research aims to analyze price volatility, volatilityspillover, and to know price trend of garlic commodity before and after free trade. This research is supported by secondary data (time series), they’re garlic price of producer (farmer) and consumer (retailer) in East Java, for 21 years, from 1992 until 2012 (monthly). To analyze price volatility is used ARCH/GARCH method, volatilityspillover is used EGARCH method, and to measure price trend is used Least Square method. The results of research are showed that before free trade price volatility of producer and consumer are high volatility, while after free trade producer price volatility is high volatility and consumer price volatility is low volatility. Before free trade indicate volatilityspillover, while after free trade don’t indicate volatilityspillover. Price trend of producer and consumer after free trade are increase very high than price trend of producer and consumer before free trade. Both of price trend are uptrend line.
The results showed that there was no significant different on soybean prices between imports prices and the U.S national prices. It was proven by the volatility of import prices (5.94 percen) that as high as of the U.S national prices (5.06 percen). Soybean price volatility at various price levels in dynamic conditions are diverging or more likely the greater at the level of farmers which is characterized by α + β> 1, with the volatility tends to spiky or surged sharply (α> β), and convergent or getting smaller on a temporary basis . Price volatility dynamically between lokal soybean prices to the price of imported soybeans are diverging with α + β> 1. It means t hat the lokal soybean prices have fluctuated very turbulent / volatile on imported soybean prices which tends to very low than lokal soybean prices or likely to occur the greater of volatility in the future. Whereas by temporary, price volatility tends to be convergent or became weaker (die down).
naturally refers to the price itself. For that reason, the price volatility for the tomato commodity in this research was performed through the analysis on the standard of deviation. Meanwhile, the linier regression was employed to analyze the price transmission in the context of producers and consumers, in addition, the test for spillovervolatility of the tomato price within the producer and the consumer contexts was executed by using GARCH approach.
Singapore which is part of the group of de- veloped countries in the ASEAN region plays an important role as the information leader in this regional market and therefore is very pos- sible in creating a price and volatilityspillover from the Singapore stock exchange into other markets in this region, including Indonesia. This article studies the asymmetry spillover volatili- ties between the Singapore and Indonesia stock exchange. Many empirical findings have shown that the spillovervolatility among markets is asymmetric. Bae and Karolyi (1994) have found that volatilityspillover from the United States to the Japanese stock market is greater after a negative return innovation (bad news) compared to after a positive return innovation (good news). Koutmos and Booth (1995) have also found the existence of the same asymme- try at the volatility transmission between New York, Tokyo, and the London stock exchang- es. Kansas (1998) also found the asymmetric pattern at the volatility transmission between London, Frankfurt, and Paris stock exchanges. Other studies have analyzed the asymmetric pattern in the transmission of the volatility dur- ing the bull and bear market period. The result of these observations shows that volatility from one market into another market will be trans- mitted faster and stronger during the downward market movement phase.
Onion price fluctuations are unstable and unpredictable because of the shock on the demand and supply cause price volatility. High and unpredictable fluctuating prices closely related to the producer and the consumer market because prices are formed of demand and supply. Unpredictable price also allegedly provide opportunities for traders to manipulate prices at the farm level information so that the price has not been completely transmitted from the consumer to the farmer's market. Associated with volatility and price transmission in the onion producer and consumer level then performed an analysis of volatilityspillover. The method used are ARCH / GARCH, VAR, and GARCH-BEKK. The results showed that the price volatility that occurs at the level of producers and consumers is low volatility but volatility at the producer level is lower than at the consumer level. Results also showed there are prices transmission and volatilityspillover at the producer and consumer prices.
The FEVD simulation results (Table 5) show that overall in the short term the most dominant variant contributing in explaining the Indonesian paddy production is the paddy production shock itself (approximately 52percent in the early 12months period), followed by the temperature changes (reaching 38.39 percent on the same period above).The contribution of the temperature to the paddy production variable is theoretically explained from the supply side. Mean while the most dominant variant contribution in explaining the Rice price volatility is also the rice price volatility itself, (approximately 72 percent in the early 12 months period), then followed by the paddy production variables (reaching 21.01 percent). The temperature changes only contribute approximately 6.17 percent to it. This means that the direct factor that influences the rice price fluctuation is still the paddy production, as it was derived from the supply side factors affecting the rice price in Indonesia.
Emerging from the influential work of Ang et al. (2006) a considerable number of studies confirm a negative cross-sectional correlation between idiosyncratic volatility and stock returns (see, for instance, Fu, 2009, Hou and Loh, 2016, and references therein). This finding is denoted as the “idiosyncratic volatility puzzle”, since asset pricing theory suggests an opposite outcome. Either investors’ portfolios are well diversified in equilibrium or investors are underdiversified. In the first case, idiosyncratic risk is diversified and the only risk to be priced is systematic. In the second case, idiosyncratic risk matters and investors with standard risk-return preferences asked for a premium to compensate for bearing risk. Starting from theory it would thus be most reasonable to expect either no relation between idiosyncratic volatility and stock returns or a positive relation. As demonstrated in Hou and Loh (2016) this idiosyncratic volatility puzzle has, to a substantial extent, remained unsolved.
Diketahui dari literatur, bahwa data return selain mengandung volatility clustering, data return juga tidak terdistribusi normal tetapi mengandung fat-tails distribution, maka data tersebut memiliki kecenderungan lebih sulit untuk diukur. Distirbusi yang bersifat fat-tails dapat terlihat jika Kurtosisnya memiliki nilai positif lebih dari 3. Distribusi yang memiliki karakteristik seperti yang disebutkan sebelumnya, dapat di golongkan sebagai leptokurtik. Kondisi tersebut mencerminkan bahwa data memiliki karakteristik yang berbeda dari distribusi normal (Gaussian). Oleh karena itu, untuk dapat meneliti dengan data yang berkarakteristik seperti yang telah disebutkan, kami menggunakan model T-GARCH untuk meneliti volatilitas return saham dari bursa Indonesia. Model T-GARCH merupakan pengembangan dari model ARCH/GARCH oleh Engle (1992) dan Bolerslev (1986) yang dikembangkan oleh Zakoian (1991). Hansen dan Lunde (2001) telah menguji sebanyak 330 kali model GARCH dengan ordo berbeda dan ditemukan bahwa GARCH (1,1) merupakan model terbaik untuk memprediksi volatilitas dan return. Untuk itu, T- GARCH (1,1) digunakan dalam penelitian ini untuk menganalisa volatilitas return saham Indonesia, Malaysia dan Singapura.
Another cause for concern has been increased volatility in global financial and foreign exchange markets. Tanzania remained largely unscathed by previous financial market turbulences due to its limited financial development and global integration. However, since the country is drifting towards deeper financial integration, with rising private capital flows and external commercial borrowing as well as pending sovereign bond issuance, it has become increasingly prone to global market instabilities. A closer scrutiny thus seems warranted in light of surfacing concerns that increased global financial volatility might put a drag on Tanzania’s growth pace. Further, since the early 2015, the Tanzanian Shilling has seen significant depreciation on the back of a strong dollar appreciation and, to a limited extent, declining aid inflows. Hence the need for examining whether the sharp nominal depreciation has been associated with higher inflation.
These aspects are addressed in our recent work (Andreou and Ghysels 2013; Ghysels 2013). We provide the asymptotic analy- sis of common volatility risk factor estimation using large panels of filtered volatilities such as (1) parametric spot volatility filters (e.g., ARCH-type models) or (2) data-driven realized volatili- ties (e.g., Zhang 2001; Andreou and Ghysels 2002; Mykland and Zhang 2008) and data-driven spot volatilities (e.g., Zhang, Mykland and A¨ıt-Sahalia 2005; Hansen and Lunde 2006). Un- like Hu and Tsay (2013), who consider the cumulative gener- alized kurtosis matrix based on cross-products of y t , we esti-
This study was conducted to determine the effect of capital structure on firm’s value and performance of shares. The firm’s value is measured using the ratio of Tobin's Q. The perfomance of portion measured using votality of stock price, volume, and trading frequency. This study uses 49 manufacturing companies are actively traded in Indonesian stock exchange. The result of this study consistent with theory of capital structure that is trade off theory. Investors see an increase in short term debt as a financial cost. But, investors are seeing an increase in long term debt as leverage. This can be seen from the stock trading in Indonesian stock exchange. The debt will increase the volatility of stock prices and lower frequency stock trading as of the date of publication. But, the increase in debt would be increase the volume and decrease the frequency of stock trading for the next period.
These event-study papers have found that after a security hits a price limit, the volatility of that security is lower on subsequent days (e.g., Ma et al., 1989a,b). While this finding suggests that price limits may have played a role in mitigating volatility, Lehmann (1989) and Miller (1989) correctly point out that we would naturally observe lower volatility on days following high volatility days. Thus, while event studies are potentially insightful, they are subject to some interpretation problems, especially because stock price volatility is well known to be serially dependent (Chen, 1998; Kim and Limpaphayom, 2000; Lehmann, 1989; Miller, 1989; France et al., 1994). Further, these event studies also suffer from a sample selection bias because securities that hit price limits on consecutive days are often excluded from the study samples (Lehmann, 1989; Miller, 1989; and Kim and Rhee, 1997).
Penelitian ini menganalisis sumber peningkatan produktivitas perusahaan lokal indonesia dengan adanya penanaman modal asing di Industri Tekstil Indonesia periode 2007-2013. Industri Tekstil memiliki kode KBLI 170 dan 130. Penelitian ini mengaplikasikan analisis kuantitatif dengan data survey perusahaan manufaktur Industri menengah dan besar yang dilakukan oleh Badan Pusat Statistik Indonesia. Metode yang digunakan dalam mengolah data dibagi menjadi dua, yakni metode linier programming non parametrik dan metode regresi data panel. Jumlah perusahaan yang diobservasi berjumlah 325 perusahaan. Hasil penelitian menunjukkan bahwa sumber utama peningkatan produktivitas adalah Perubahan Efisiensi Teknis, namun setiap tahunnya kemajuan teknologi mengalami peningkatan selama periode observasi. Penanaman Modal Asing memberikan dampak Spillover positif pada Perubahan Efisiensi Teknis, namun memberikan dampak negatif pada Perubahan Efisiensi Skala dan Perubahan Teknologi.
Phavaskar et al (2013) argue that Extra liquidity through QEs, in a method similar to that of osmosis, often goes beyond domestic boundaries and flows to capital-parched emerging market (EM) economies, offering a higher return on investment. Ahmed & Zlate (2012) found statistically not significant effects of unconventional U.S. monetary policy expansion on total net inflows of capital into EMEs. Furthermore, Fernandes (2012) argues that Quantitative easing have positive impact on Gold. Based on the phenomenon above and studies above, then the concern of this research is to analyze the impact of USD money supply on the exchange rate volatility, IHSG volatility, and the gold price volatility.