Modeling stock prices in a portfolio using multidimensional geometric brownian motion

Maruddani, Di Asih I; Trimono,

doi:10.1088/1742-6596/1025/1/012122

Cited by 12 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to test our predictions, we employ the Mean Absolute Percentage Error (MAPE) (Reddy and Clinton 2016;Maruddani and Trimono 2018)…”

Section: Resultsmentioning

confidence: 99%

“…Also interesting to note, there are many instances of correlations being much more extreme than the "coincidental" correlations for small displacements, indicating a possible correlation for small changes in time. While our equations operate on normal random variables, there is evidence to suggest other distributions may be a more appropriate fit (Mantegna and Stanly 2000). To test this, we first estimate the parameters using MLE and then extract and measure the noise variables from each time series.…”

Section: Mle For the Gou Processmentioning

confidence: 99%

“…In this work, we aim to expand upon the study of (correlated) stochastic differential equations (SDEs) and their application to cryptocurrency markets. A large number of studies have demonstrated that complex socio-economic systems (Máté and Néda 2016), more specifically stocks (Merton 1973;1971;Black and Scholes 1973;Wilmott et al 1995;Bouchaud and Potters 2000;Mantegna and Stanly 2000;Reddy and Clinton 2016;Øksendal 2003), commodities (Schwartz 1997;Mejía Vega 2018), and cryptocurrencies (Cretarola and Figà-Talamanca 2019b; can be modeled using SDEs. Further, correlations do exist between some stocks (Mantegna and Stanly 2000;Teng et al 2016;Plerou et al 2002;Sándor and Néda 2015;Onnela et al 2003), various cryptocurrencies (Saha 2018;Chaim and Laurini 2019), and possibly exist between online social media activities.…”

Section: Introductionmentioning

confidence: 99%

“…There is, however, debate on the extent these assets follow GBM dynamics. While GBM operates using Gaussian noise, there is evidence that the noise distribution of stocks such as the S&P 500 has curvature different than a normal distribution (Mantegna and Stanly 2000). Implication of this difference is most prominent in the frequency of rare events as a result of heavy-tailed noise, also possibly present for cryptocurrencies (Kreuser and Sornette 2018;Wu et al 2018;Fry 2018).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

et al. 2020

View full text Add to dashboard Cite

The growing interconnectivity of socioeconomic systems requires one to treat multiple relevant social and economic variables simultaneously as parts of a strongly interacting complex system. Here, we analyze and exploit correlations between the price fluctuations of selected cryptocurrencies and social media activities, and develop a predictive framework using noise-correlated stochastic differential equations. We employ the standard Geometric Brownian Motion to model cryptocurrency rates, while for social media activities and trading volume of cryptocurrencies we use the Geometric Ornstein-Uhlenbeck process. In our model, correlations between the different stochastic variables are introduced through the noise in the respective stochastic differential equation. Using a Maximum Likelihood Estimation on historical data of the corresponding cryptocurrencies and social media activities we estimate parameters, and using the observed correlations, forecast selected time series. We successfully analyze and predict cryptocurrency related social media and the cryptocurrency market itself with a reasonable degree of accuracy. In particular, we show that our method has impressive accuracy in predicting whether a cryptocurrency market will increase or decrease a day in the future, a significant result with regards to investing and trading cryptocurrencies.

show abstract

“…In order to test our predictions, we employ the Mean Absolute Percentage Error (MAPE) (Reddy and Clinton 2016;Maruddani and Trimono 2018)…”

Section: Resultsmentioning

confidence: 99%

Section: Mle For the Gou Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

et al. 2020

View full text Add to dashboard Cite

show abstract

“…According to Maruddani & Trimono (2018), MAPE is a method commonly used to evaluate predictive value by considering the effect of actual value. The MAPE equation is as follows:…”

Section: Model Performance Measuresmentioning

confidence: 99%

Forecasting Farmer Exchange Rate in Central Java Province Using Vector Integrated Moving Average

Trimono

Sonhaji

Mukhaiyar

2020

Medstat

View full text Add to dashboard Cite

Farmer Exchange Rate (FER) is an indicator that can be used to measure the level of farmers welfare. For every agriculture sector, FER is affected by the historical price of harvest from the corresponding sector and historical prices of other agriculture sectors. In Central Java Province, rice & palawija, horticulture, and fisheries are the largest agriculture sectors which is the main livelihood for most of the population. FER forecasting is a crucial thing to determine the level of farmers welfare in the future. One method that can be used to predict the value of a variable that is influenced by the historical value of several variables is Vector Time Series. An empirical study was conducted using FER data from the rice & palawija, horticulture and fisheries sectors for January 2011-June 2017 in Central Java Province. The results obtained show that by using the VIMA(2.1) model, the FER prediction was very accurate, with MAPE values were 1.91% (rice & palawija sector), 2.44% (horticulture sector), and 2.18% (fisheries sector).

show abstract

Multi-asset option pricing using an information-based model

2020

View full text Add to dashboard Cite

Modeling stock prices in a portfolio using multidimensional geometric brownian motion

Cited by 12 publications

References 9 publications

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

Forecasting Farmer Exchange Rate in Central Java Province Using Vector Integrated Moving Average

Multi-asset option pricing using an information-based model

Contact Info

Product

Resources

About