scholarly journals Analysing the Nepali Stock Market with Stochastic Models

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Karan Singh Thagunna ◽  
Radal M Lochowski
Keyword(s):  
Brownian Motion ◽  
Stock Market ◽  
Diffusion Model ◽  
Stock Prices ◽  
Jump Diffusion ◽  
Efficient Market Hypothesis ◽  
Geometric Brownian Motion ◽  
Brownian Motion Model ◽  
Jump Diffusion Model ◽  
Daily Returns

In this article we analyse the behaviour of the Nepali stock market and movements of stock prices of selected companies using (i) Efficient Market Hypothesis (EMH) (ii) geometric Brownian motion model (gBm) and (iii) Merton’s jump-diffusion model. Using the daily returns of the NEPSE index and the daily returns of stock prices of selected companies we estimate the geometric Brownian motion model and Merton’s jump-diffusion model. Further, we compare both models to identify the best fit for the Nepali stock market data. Keywords: Black-Scholes model, Efficient Market Hypothesis, geometric Brownian motion, Merton’s jump-diffusion Model, Variance Ratio Test

scholarly journals Prediksi harga saham PT. Astra agro lestari TBK dengan jump diffusion model

2017 ◽  
Vol 3 (1) ◽  
pp. 57 ◽  
Author(s):  
Di Asih I Maruddani ◽  
Trimono Trimono
Keyword(s):  
Brownian Motion ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Geometric Brownian Motion ◽  
Jump Diffusion Model

Saham merupakan salah satu emiten yang paling banyak diperjualbelikan di pasar modal. Harga saham dan perubahannya merupakan dua indikator yang sering dijadikan bahan pertimbangan oleh para calon investor sebelum memutuskan untuk membeli saham suatu perusahaan. Harga saham hampir selalu mengalami perubahan, dan sulit diperkirakan bagaimana keadaannya pada periode yang akan datang. Terdapat berbagai metode yang dapat digunakan untuk memperikirakan harga saham pada periode yang akan datang. Diantaranya adalah pemodelan dengan Geometric Brownian Motion (GBM) dan pemodelan dengan GeometricBrownian Motion (GBM) dengan Jump. Metode GBM dapat memperediksi harga saham dengan baik apabila data return saham periode sebelumnya berdistribusi normal. Sedangkan jika pada data return saham periode sebelumnya memenuhi asumsi normalitas dan ditemukan adanya lompatan, maka digunakan metode Jump Diffusion. Prediksi harga saham AALI untuk periode 03/01/2017 sampai dengan 12/05/2017 dengan GBM menghasilkan akurasi peramalan yang baik, dengan nilai MAPE sebesar 11,26%. Prediksi harga saham AALI untuk periode 03/01/2017 sampai dengan 12/05/2017 dengan metode Jump Diffuison menghasilkan akurasi peramalan yang sangat baik, dengan nilai MAPE sebesar 2,60%. Berdasarkan nilai MAPE, model Jump Diffusion memberikan hasil yang lebih baik daripada model GBM.

Jump diffusion model with application to the Japanese stock market

2008 ◽  
Vol 78 (2-3) ◽  
pp. 223-236 ◽  
Author(s):  
Koichi Maekawa ◽  
Sangyeol Lee ◽  
Takayuki Morimoto ◽  
Ken-ichi Kawai
Keyword(s):  
Stock Market ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Jump Diffusion Model ◽  
Japanese Stock Market

CDS Premium and Jump Risk in Stock Market

2012 ◽  
Vol 20 (3) ◽  
pp. 347-364
Author(s):  
Kook-Hyun Chang ◽  
Byung-Jo Yoon
Keyword(s):  
Stock Market ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Conditional Volatility ◽  
Jump Risk ◽  
Premium Rate ◽  
Korean Stock Market ◽  
Jump Diffusion Model

This paper tries to empirically investigate whether the jump risk of Korean stock market may be statistically useful in explaining the Korean CDS (5Y) premium rate. This paper uses the jump-diffusion model with heteroscedasticity to estimate the conditional volatility of KOSPI from 7/2/2007 to 7/30/2010. The total volatility of Korean stock market is decomposed into a heteroscedasticity and a jump risk by using the jump-diffusion model. The finding is that the jump risk in stead of heteroscedasticity in Korean stock market can explain the Korean CDS premium rate.

scholarly journals Comparison of Stochastic Forecasting Models

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Brownian Motion ◽  
Stock Prices ◽  
Absolute Error ◽  
Geometric Brownian Motion ◽  
Time Dependent ◽  
Confidence Bounds ◽  
History Of ◽  
Brownian Motion Model ◽  
Stochastic Forecasting ◽  
Two Parameters

In this work, we compare several stochastic forecasting techniques like Stochastic Differential Equations (SDE), ARIMA, the Bayesian filter, Geometric Brownian motion (GBM), and the Kalman filter. We use historical daily stock prices of Microsoft (MSFT), Target (TGT) and Tesla (TSLA) and apply all algorithms to try to predict 54 days ahead. We find that there are instances in which all algorithms do well, or do poorly. We find that all three stocks have a strong auto-correlation and a high Hurst factor which shows that it is possible to predict future prices based on a short history of past prices. In our geometric Brownian motion model, we have two parameters for drift and diffusion which are not time dependent. In our more general SDE model (TDNGBM), we have time-dependent drift and time-dependent diffusion terms which makes it more effective than GBM. We measure all algorithms on the correlation between the predicted and actual values, the mean absolute error (MAE) and also the confidence bounds generated by the methods. Confidence intervals are more important than point forecasts, and we see that TDNGBM and ARIMA produce good bounds.

scholarly journals Fuzzy Time Series and Geometric Brownian Motion in Forecasting Stock Prices in Bursa Malaysia

2019 ◽  
Vol 14 (2) ◽  
pp. 240-250
Author(s):  
Nor Hayati Shafii ◽  
Nur Ezzati Dayana Mohd Ramli ◽  
Rohana Alias ◽  
Nur Fatihah Fauzi
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Geometric Brownian Motion ◽  
Fuzzy Time Series ◽  
Good Information ◽  
The Mean

Every country has its own stock market exchange, which is a platform to raise capital and is a place where shares of listed company are traded. Bursa Malaysia is a stock exchange of Malaysia and it is previously known as Kuala Lumpur Stock Exchange. All over the world, including Malaysia, it is common for investors or traders to face some loss due to wrong investment decisions. According to the conventional financial theory, there are so many reasons that can lead to bad investment decisions. One of them is confirmation bias where an investor has a preconceived notion about an investment without a good information and knowledge. In this paper, we study the best way to provide good information for investors in helping them make the right decisions and not to fall prey to this behavioral miscue. Two models for forecasting stock prices data are employed, namely, Fuzzy Time Series (FTS) and Geometric Brownian Motion (GBM). This study used a secondary data consisting of AirAsia Berhad daily stock prices for a duration of 20 weeks from January 2015 to May 2015. The 16-weeks data from January to April 2015 was used to forecast the stock prices for the 4-weeks of May 2015. The results showed that FTS has the lowest values of the Mean Absolute Percentage Error (MAPE) and the Mean Square Error (MSE), which are 1.11% and MYR20.0011, respectively. For comparison, for GBM, the MAPE is 1.53% and the MSE is MYR2 0.0017. The findings imply that the FTS model provides a more accurate forecast of stock prices. Keywords: Forecasted values, stock market, Fuzzy Time Series, Geometric Brownian Motion

scholarly journals Estimation for a Second-Order Jump Diffusion Model from Discrete Observations: Application to Stock Market Returns

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Tianshun Yan ◽  
Yanyong Zhao ◽  
Shuanghua Luo
Keyword(s):  
Stock Market ◽  
Diffusion Model ◽  
Jump Diffusion ◽  
Second Order ◽  
Model Parameters ◽  
Ratio Test ◽  
Market Returns ◽  
Discrete Observations ◽  
Stock Market Returns ◽  
Jump Diffusion Model

This paper proposes a second-order jump diffusion model to study the jump dynamics of stock market returns via adding a jump term to traditional diffusion model. We develop an appropriate maximum likelihood approach to estimate model parameters. A simulation study is conducted to evaluate the performance of the estimation method in finite samples. Furthermore, we consider a likelihood ratio test to identify the statistically significant presence of jump factor. The empirical analysis of stock market data from North America, Asia, and Europe is provided for illustration.

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