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"This groundbreaking text has been augmented with new material and fully updated to prepare students for the new-style MLC exam."

"Managing financial risks comes down to understanding how to reduce a complex business environment into workable concepts and models. "The AMA Handbook of Financial Risk Management" provides readers with the tools they need for dealing with the most important areas of financial decision making. Filled with strategies, principles, and measurement techniques, the book shows readers how to: categorize financial risks; reduce risks from cash flow and budget exposures; analyze operating risks; understand assessments or risk and return; and, manage risks in capital investment decisions. Providing both explanations and practical applications, the book clarifies the factors that affect the value of a firm, considerations such as time and the proper use of debt, and risks inherent in the capital structure of the firm and the valuation of business combinations. This is a comprehensive guide that enables risk managers and anyone involved in the financial management of an organization to know what factors are at stake and how to protect their bottom line.;"

"ContentsForeword -- Preface -- Acknowledgments -- Introduction -- Institutional background -- Operational risk -- Financial disasters -- The systemic disaster of 2007-2008 -- Managing financial risk -- VaR and stress testing -- Model risk -- Managing spot risk -- Managing forward risk -- Managing vanilla options risk -- Managing exotic options risk -- Credit risk -- Counterparty credit risk -- Bibliography -- About the companion website -- Index."

"BPK telah mengimplementasikan Sistem Informasi Pemantauan Tindak Lanjut (SIPTL) untuk melaksanakan dan memantau tindak lanjut rekomendasi hasil pemeriksaan. Sejalan dengan mandat yang diberikan Undang-Undang Dasar 1945 untuk melaksanakan pemeriksaan atas pengelolaan dan tanggung jawab keuangan negara secara bebas dan mandiri, keamanan informasi hasil pemeriksaan merupakan hal penting bagi BPK. Namun demikian, dalam operasionalnya, pemanfaatan SIPTL belum sesuai dengan standar manajemen risiko keamanan informasi. Penelitian ini bertujuan untuk mendapatkan rancangan manajemen risiko keamanan informasi SIPTL. Penelitian ini menggunakan metode kualitatif dan pengumpulan data melalui wawancara dan studi literatur. Wawancara dilakukan dengan pejabat eselon III dan IV pada Biro TI BPK. Kerangka kerja yang digunakan pada penelitian ini berdasarkan SNI ISO/IEC 27005:2018 dengan penanganan risiko menggunakan SNI ISO/IEC 27001:2013, dan SNI ISO/IEC 27002:2013. Hasil yang didapatkan dari penelitian ini adalah 13 skenario risiko di mana dua risiko mempunyai level yang tinggi, lima risiko mempunyai level sedang, dan enam risiko memiliki level rendah. Berdasarkan skenario risiko selanjutnya disusun rancangan manajemen risiko keamanan informasi SIPTL, yang dapat digunakan sebagai bahan pertimbangan dalam penerapan manajemen risiko keamanan informasi di BPK.

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BPK has implemented the Follow-up Monitoring Information Systems (SIPTL) to conduct and monitor follow-up of recommendations-audit result. In line with the mandate given by the 1945 Constitution to audit towards management of and accountability for the state’s finances a free and independent, the information security of audit results is an important matter for BPK. However, in its operations, the utilization of SIPTL is not in accordance with information security risk management standards. This study aims to obtain a SIPTL information security risk management design. This research uses qualitative methods and data collection through interviews and literature studies. Interview was conducted with middle level official at BPK’s Bureau of IT. The framework used in this research is based on SNI ISO / IEC 27005: 2018, and risk treatment based on SNI ISO / IEC 27001: 2013 also SNI ISO / IEC 27002: 2013. The results obtained from this study are 13 risk scenarios including two high level risks, five medium level risks, and six low level risks. Based on the risk scenario, the SIPTL information security risk management design is then prepared, which can be used as recommendation towards the implementation of information security risk management at BPK.

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"Salah satu alat ukur risiko yang memiliki peran penting dalam manajemen portofolio adalah Value-at-Risk VaR . VaR didefinisikan sebagai jumlah kerugian portofolio yang mungkin terjadi dengan tingkat kepercayaan yang tinggi, selama periode waktu tertentu. Secara matematis, VaR adalah persentil dari distribusi loss. Secara umum, return pergerakan harga saham dimodelkan dengan gerak Brown. Sementara itu, distribusi loss dari instrumen keuangan lebih berisfat leptokurtic dari distribusi normal dan cenderung memiliki fat tails . Oleh karena itu, karakteristik dari distribusi loss tersebut tidak memenuhi asumsi distribusi normal. Dengan demikian, proses Variance Gamma VG adalah proses stokastik alternatif untuk mendeskripsikan model dari distribusi return harga saham. Proses VG didefinisikan sebagai gerak Brown dengan perubahan waktu acak mengikuti proses Gamma. Pada penerapannya dalam pasar modal, perhitungan VaR akan dilakukan pada Indeks Harga Saham Gabungan Indonesia IHSG .

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One of the measures of risk which has an important role in managing portfolio is Value at Risk VaR . VaR is defined as the amount of possible portfolio losses with a high level of certainty, over a specific time frame. From statistical point of view, VaR is the percentile of the loss distribution. In general, return of the stock prices is modeled with Brownian motion. Meanwhile, return distributions of financial instruments are more leptokurtic than normal distribution and tend to have the fat tails . Therefore, these characteristics of return distributions are countering the normality assumption. Accordingly, a Variance Gamma VG process is an alternative stochastic process to describe the model for the return distribution of stock prices. This process is defined as Brownian motion with random time change following gamma process. On purpose of risk management application, the calculation of VaR will be carried out by using Indonesia Composite Index IDX . "

"Dalam rangka mewujudkan sistem pembayaran yang efisien, cepat, aman dan handal, Bank Indonesia selaku Bank Sentral telah menerapkan sistem pembayaran berupa Real-Time Gross Settlement (RTGS). Sistem ini telah diterapkan hampir disebagian besar wilayah Asia Pasifik yang meliputi Hong Kong, Korea, Australia, China, New Zealand, dan Thailand. Di Indonesia sistem ini dikenal dengan sebutan BI-RTGS.Selain sistem BI-RTGS, Bank Indonesia juga memberikan sistem pembayaran nasional berupa Sistem Kliring Nasional Bank Indonesia (SKNBI). Diharapkan melalui kedua sistem tersebut akan tercipta pembayaran yang efisien, cepat, aman dan handal.Peraturan Bank Indonesia (PBI) Nomor 6/8/PBI/2004 tanggal 11 Maret 2004 tentang Sistem BI-RTGS dan PBI Nomor 7/18/PBI/2005 tanggal 22 Juli 2005 tentang SKNBI telah mengadopsi standar international business practice maupun core principle BIS. Dengan demikian Bank XYZ sebagai peserta sistem BI-RTGS dan SKNBI wajib tunduk kepada kedua PBI tersebut di atas dan sudah barang tentu menimbulkan konsekuensi yaitu timtutan agar pegawai Bank XYZ yang ditempatkan pada operasional sistem pembayaran bekerja lebih teliti, hati-hati dan seksama agar dapat meminimalkan atau meniadakan risiko yang mungkin dapat muncul dalam pelaksanaan transfer dana melalui kedua sistem dimaksud.Kesalahan pengiriman dana untuk rekening atau nama nasabah penerima yang dituju dipeserta penerima ataupun kesalahan dalam nilai nominal dan double pengiriman adalah beberapa contoh dari risiko operasional dari sisi peserta pengirim, sedangkan kesalahan dalam menentukan judgement terhadap perintah kiriman dana masuk dari peserta pengirim merupakan contoh risiko operasional dari sisi peserta penerima.Adanya kewajiban dari peserta pengirim untuk menerbitkan perintah kiriman dana baru kepada rekening yang dituju atau nasabah penenma yang benar tanpa menunggu pengembalian dana membuat pengalokasian dana cadangan untuk risiko operasional sangat penting. Besarnya alokasi dana cadangan ini harus dihitung dengan suatu metode pendekatan yang dikenal dengan sebutan Value at Risk (VaR).Pengukuran VaR untuk risiko operasional dapat dilakukan dengan beberapa metode pendekatan dan yang sederhana, sedikit komplek, dan sangat komplek. Tingkat keakuratan pengukuran terhadap aktual loss berbanding lurus dengan tingkat kompleksitas metode yang diterapkan. Adapun metode pengukuran risiko operasional dibagi menjadi 2, yaitu metode Standard dan Advanced Measurement Approach (AMA). Metode Standard terdiri dari Basic Indicator Approach (BIA), Standardized Approach (SA), Alternative Standardized Approach (ASA). Sedangkan AMA terdiri dari Internal Measurement Approach (IMA), Loss Distribution Approach (LDA), Scoreboard Approach, Bootstrapping Approach, Bayesian Method, dan Extreme Value Theory ( EVT).Metode AMA adalah metode yang dianggap menghasilkan pengukuran risiko operasional yang lebih baik yang dapat digunakan bagi perusahaan maupun perbankan dibandingkan dengan metode-metode lainnya seperti BIA, SA, ASA. Metode AMA menggunakan pendekatan internal dalam mengukur risiko operasional, sehingga metode ini terlepas dan aturan Basel.Dalam mengukur risiko operasional perusahaan, LDA mengharuskan untuk menggunakan data kerugian operasional intemal perusahaan masing-masing. Data kerugian tersebut dikelompokkan menjadi data iiekuensi kejadian dan data severitas kerugian. Dalam metode LDA, terdapat 2 cara pendekatan pengukuran yaitu dengan pendekatan actuarial method dan aggregation method. Dalam penelitian ini, metode pengukuran yang digunakan adalah dengan menggunakan AMA-LDA aggregation method.Dari hasil pengukuran yang dilakukan dan setelah dilakukan uji back resting, dapat ditarik kesimpulan bahwa penerapan metode pengukuran AMA-LDA aggregation method cocok diterapkan bagi Bank XYZ untuk mengukur besamya cadangan dana yang hams disediakan akibat potensi kerugian risiko operasional dalam sistem pembayaran nasional di keempat cabang Bank XYZ.

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In order to achieve efficient, fast, secured and reliable payment system, Bank of Indonesia as the regulator has applied Real-Time Grass Settlement (RTGS) payment system. This system has been applied in most countries among Asia-Pasitic region, including Hong Kong, Korea, Australia, China, New Zealand, and Thailand. In Indonesia, this system is known as BI-RTGS.

Besides BI-RTGS system, Bank of Indonesia also provides national (domestic) payment system which is known as ?Sistem Kliring Nasional Bank Indonesia (SKNBI)? or ?National Clearing System of Bank Indonesia". By applying both systems, It is expected that we can have an efficient, quick, secured and reliable payment system.

Peraturan Bank Indonesia (PBI) or Bank of Indonesia Policies point 6/8/PBI/2004 on March 11th 2004 regarding to BI-RTGS System and PBI point 7/18/PBI/2005 on July 22nd 2005 regarding to SKNBI have adopted intemational business practice and BIS core principle standard. Thercby Bank XYZ as participant of BI-RTGS and SKNBI systems is required to obey both PBI above and get consequence, that the officers of Bank XYZ at operational payment system are required to work more accurately and carefully in order to minimize or eliminate any risks which might emerge in fund transfer involving both systems.

Mistaken account or customer name of fund recipient, mistaken nominal value and double transfer are some examples of operational risks on the side of fund sender. In the other hand, miss-judgement against incoming fund transfer order is the operational risk on the side of fund recipient.

Obligation of fund sender to issue order for new fund transfer to directed account or recipient without waiting for fund retum makes it very important to have spare fund allocation for operational risk. The amount of the spare fund has to be calculated using a method known as Value at Risk (VaR).

VaR measurement for operational risk can be performed using simple methods, slightly more complex methods or very complex methods. More complexity of the used method means higher accuracy level of the measurement against actual loss. There are two types of operational risk measurement methods: Standard and Advanced Measurement Approach (AMA). Standard method includes Basic Indicator Approach (BIA), Standardized Approach (SA), and Altemative Standardized Approach (ASA). AMA method includes Intemal Measurement Approach (IMA), Loss Distribution Approach (LDA), Scoreboard Approach, Bootstrapping Approach, Bayesian Method, and Extreme Value Theory (EVT).

AMA is a method which is considered as the one provides better operational risk measurement, that can be used in banking or other companies, compared with other methods, such as BIA, SA and ASA. AMA method uses internal approach in measuring operational risk, making this method not to depend on Basel rule.

In measuring company operational risk, usage of LDA demands intemal operational loss data of each company to be used. This loss data is grouped into occurrence frequency data and loss severity data. In LDA method, there are two methods of measurement, namely actuarial method and aggregation method. In this research, the method to be used is AMA- LDA aggregation method.

From the measurement and back testing result, we can get conclusion that applying AMA-LDA aggregation method is lit for Bank XYZ in measuring the amount of spare fund that must be provided due to potential loss of operational risk in national payment system which is used in four of its branches."

"The objective of this research is to examine maximum losses when investor invests on syariah based stock. Markowitz model is used for constructing the optimal portfolio. Value at Risk Model is also used for calculating the expected losses. The research indicates that volatility seems to cluster in a predictable fashion. Therefore the research forecasts variances used exponentially weighted moving average (EWMA) model. This research also aims to evaluate whether the EWMA model can predict variances reasonably well. The data used in this research are syariah based stock which had been included in Jakarta Islamic Index (JII) during the year 2005-2006. This research provides that VAR models using an EWMAforecast are good enough for predicting risk. The number of exception of 508 daily datas are only less than 5% or valid at confident level 95%. As benchmark we also use historical method and monte carlo simulation to compare performance of EWMA forecast."